Before understanding how to become an ethical hacker, let us understand more about the role.

We can call this era as Cyber Era, Not Because of the Cyber Attacks, but because of its advantages and massive growth of technology,

People showing more enthusiasm toward Hacking & Cyber Security, and there isn’t any single reason behind it,

You might probably watch movies where Hackers are doing a lot of cool stuff like hacking into banks and stealing millions and tracking people, finding out their information

But Wait..!

Hackers are usually different from what you see in the Movies, Some people often judge Hackers as Real Hackers and Fake Hackers

That isn’t a big issue

By the end of the post, you will get a clear picture of what is Hacking and how you can be a hacker

I came across many people, who often think to be a hacker, they think hacking is just like playing games with the PC but it’s not like that

However people sometimes fall in confusion, they wanted to be a hacker but due to lack of knowledge, they even ask where to start from and how.

If you’re too in the same situation, and you’re too wondering how to be a computer hacker then this post may be good to go with, I will share the step-by-step guide along with my personal experience. 

Recommend: Free Ethical Hacking Tutorial For Beginners

What is Hacking?

When some people hear the term “Hacking” they usually think this matrix effects and blindly say this is Hacking,
While others think stealing bank account balance is Hacking, defacing websites is Hacking

Don’t get confused, Hacking is not like what you see in the movies

In reality, it’s quite different

There is no single definition for Hacking, you may find different definitions on the internet

Don’t be confused, let me tell you what hacking is and how to be a computer hacker
In simple words, Computer hacking is a process of getting unauthorized access to any Computer by breaking its security

I mean breaking computer security is Hacking

It can be hacking into digital devices, spreading malware, stealing bank logs and so, it has no particular branch, it has several branches and several types of Hacking,

Related: Top 10 BEST Ethical Hacking Courses For Beginners 

Who are Hackers?

Let me explain in simple words

A person can be called as a hacker if he knows how to break the computer security

People often think Hackers are Cybercriminals and some others think Hackers are Cyber Security experts and they will work for Government only

If you’re too thinking the same then you’re wrong..!

Yes, you’re wrong

But wait.. here is a simple logic you have to understand

All Apples are fruits but all fruits are not Apples
So

Not all Hackers are Cyber Criminals, nor all Hackers are Works for the government
Some people even blindly say Ethical Hackers only work for the Government

If you’re too agree with it?

Of course, may not. but It depends
Let’s see what are the types of Hackers

Recommended Books : Python for the Ethical Hacker: Unleashing the Power of Programming

Types of Hackers

They are mainly of 3 types but are not limited to it

• White Hat Hackers
• Black Hat Hackers
• Grey Hat Hackers

White hat Hackers(Ethical Hacker)

White Hat Hackers also are known as the Ethical Hackers, they are considered as good Hackers they usually Hack to find out the vulnerability in order to improve security, in the other hand they can work for big companies, government organizations, depending on their decisions they may not work for anyone,

They can be Cyber Security experts, Security Auditors, Pentesters, and so on

Black hat Hackers

Black hat Hackers are considered as bad guys, they usually Hack for fun and profit along with a desire to destroy

You may call them Cybercriminals, as they never follow any ethical rules

Grey Hat Hackers

Grey Hat Hackers are those who hack for political issues, they usually deface websites and they leave freedom of speech

Hackvisits

Hackvisits are a group of strongly motivated Hackers, they are maybe good or bad, it depends on their group and their mindset

Hope you have got a clear understanding of what is Hacking and who are Hackers.

Related: What is Hacking? What are the types of Hackers 

Why do you want to be an Ethical Hacker? 

Guys, If you want to be ethical hackers just for money Because the salary of hackers is very large. Then sorry, this option is not for you. Because no doubt that hackers get a high-salary job. But hacking is not very easy. It is so difficult. no doubt, you can get a high-salary job, but the main thing is learning to hack.

So, If you want to be a hacker for your interest and you knew something about hacking. Then you can easily learn to hack. Because hacking is not easy as shown in movies. You have to work with your full dedication. Then you can become an ethical hacker.

Not learn hacking just for getting a high-salary job. Just learn with your interest. Then nobody can stop you to becoming an ethical hacker or getting a high-salary job.
Let’s see how to become an ethical hacker

Recommended Guides

• What Is The Dark Web? How To Access It And What You Will Discover
• How to be Anonymous – A beginners guide 

What is Ethical Hacking?

The term “hacking” has a negative connotation, but that will only be true until the job of an ethical hacker is properly understood. Ethical hackers, often known as “white hat” hackers, are the good guys of the cyber world. So, what does an ethical hacker’s job entail? Rather than using their high computer skills for bad purposes, ethical hackers find flaws in data computer security for businesses and organizations all around the world in order to protect them against hackers with less than ethical motives.

So, let’s explore the skills required to become an ethical hacker. 

1. Computer Networking Skills

Networking skills are one of the most important talents to have if you want to be an ethical hacker. The computer network is simply the interconnection of several devices, known as hosts, that are connected via multiple paths to send and receive data or media. Understanding networks such as DHCP, Suoernetting, Subnetting, and others can allow ethical hackers to examine the many interconnected machines in a network, as well as the potential security threats that this may present, as well as how to deal with those threats. Computer Network Tutorials can help you learn about computer networking.

Networking is a broad term, and it can’t be mastered easily, still, you can learn by focusing on one by one topic

Networking contains IP protocols, Proxies, VPN, Tor, and so on

2. Computer Skills

Computer skills refer to the knowledge and abilities required to operate computers and related technology. Data processing, managing computer files, and making presentations are all examples of fundamental computer abilities. Database management, programming, and spreadsheet calculations are examples of advanced computer skills. MS Office, Spreadsheets, Email, Database Management, Social Media, Web, Enterprise Systems, and other computer skills are among the most important. A computer systems specialist is required of an ethical hacker.

3. Linux Skills

The Linux Kernel provides the foundation for a community of open-source Unix-like operating systems. It is a free and open-source operating system, and the source code can be updated and distributed to anybody under the GNU General Public License, whether commercially or noncommercially. The main reason to study Linux as an ethical hacker is that it is more secure than any other operating system in terms of security. This is not to say that Linux is completely secure; it does have viruses, but it is less vulnerable than any other operating system. As a result, no anti-virus software is required.

4. Programming Skills

Programming skills are another important talent to have if you want to be an ethical hacker. So, in the computer world, what exactly does the term programming indicate? “Writing code that a computer system understands to perform multiple instructions” is what it means. As a result, in order to improve one’s programming skills, one must write a great deal of code! Before beginning to write code, one must first determine which programming language is most appropriate for his or her needs. Here’s a summary of the programming languages used by ethical hackers, as well as where you may learn them.

As a hacker, we must have a programming background, as we have to find multiple ways to exploit

If you learn a multi-purpose programming language then you can automate many tasks

Learning programming isn’t a hard thing, but it depends

Firstly I recommend you to start with C Programming, though you can learn Python without the knowledge of C, it’s better to go with C

Also, you have to Master Web programming language,

Learn HTML, JavaScript, PHP & MySQL
These languages are easy to learn, and a hacker must have in-depth knowledge of web applications.

Programming languages that are useful to hacker

5. Basic Hardware Knowledge

Computer hardware includes the central processing unit (CPU), monitor, mouse, keyboard, computer data storage, graphics card, sound card, speakers, and motherboard, among other components. Software, on the other hand, is a set of instructions that may be stored and performed by hardware. Consider the case of trying to hack a computer-controlled machine. First, he must understand the machine’s function. Finally, he must gain access to the machine’s control computer. Now, the computer will have a strong software security system; however, hackers are unconcerned about hardware security, so if he has access to it, he can play with it. How would someone who doesn’t understand hardware understand how the motherboard works, how USBs transport data, how CMOS and BIOS function together, and so on? To become an ethical hacker, one needs also to have a fundamental understanding of hardware.

6. Reverse Engineering

The process of recovering a product’s design, requirement specifications, and functionality from an analysis of its code is known as reverse engineering. It creates a software database and uses it to generate data. The goal of reverse engineering is to speed up maintenance work by making a system more understandable and producing the essential documents for a legacy system. Reverse engineering is commonly used in software security to ensure that the system is free of major security flaws or vulnerabilities. It aids in the development of a system’s strength, hence protecting it from hackers and spyware. Some developers even go as far as hacking their system to identify vulnerabilities – a system referred to as ethical hacking. 

7. Cryptography Skills

Cryptography is the study and application of techniques for secure communication while third parties, known as adversaries, are present. It is concerned with the creation and analysis of protocols that prohibit harmful third parties from accessing information shared between two entities, therefore conforming to many principles of information security. Cryptography is the process of turning plain text into ciphertext, a non-readable form that is incomprehensible to hackers, while it is being transmitted. An ethical hacker must ensure that information between different members of the organization is kept private.

8. Database Skills

The Database Management System (DBMS) is at the heart of all database creation and management. Because accessing a database containing all of the company’s data can put the organization at risk, it’s critical to ensure that the software is hack-proof. To assist the organization in building a powerful DBMS, an ethical hacker must have a good understanding of this, as well as different database engines and data structures.

9. Problem-solving Skills

Problem-solving abilities help to identify the root of a problem and find an effective option. Aside from the technical talents listed above, an ethical hacker must also be a critical thinker and a problem solver who can solve problems quickly. They must be eager to learn new techniques and guarantee that all security flaws are thoroughly investigated. This requires a lot of testing and a creative mind for coming up with new approaches to solving problems.

10. Read Books and Online Courses

Most of the Hackers who started their Hacking career are self-learned, but in addition to their knowledge, one must keep reading and learning from various sources
You can start reading Beginner Books, also you can take online courses too

11. Set up Vulnerable labs and practice

I recommend you to start learning yourself and challenge yourself
Vulnerable labs are great options for testing your knowledge and here is an awesome guide on vulnerable labs set up by infoseinstitute

I highly recommend you to try with

• DVWA
• Mutillidae
• BWAPP

These vulnerable web applications help you to master OWASP Top 10 Vulnerabilities

12. Join Bug Bounty Platforms

If you’ve mastered the skills then it’s time to earn your skills
Just join Bug Bounty Platforms such as hackerone and BugCrowd
You can explore many bug Bounty programs just choose as you like, and earn money by finding vulnerabilities.

13. Keep updating yourself

Hacking is a long time journey of learning, we must not enter just to hack wifi password or to hack someone’s phone,

As time goes by there are new vulnerabilities discovered and patched, thus we must be eager to learn new things

My last Pro tip, Always try to learn from Public Disclosures & POCs

Hope you like this article, share this article with your friends, and you can leave a comment if you have any doubts

Other skills

In addition to programming skills, a good hacker should also have the following skills:

• Know how to use the internet and search engines effectively to gather information.
• Get a Linux-based operating system and know the basic commands that every Linux user should know.
• Practice makes perfect, A skilled hacker should put in long hours and contribute positively to the hacker community. He or she can help by writing open-source software, answering queries on hacking communities, and so on. 

What is the Attraction of an Ethical Hacking Career?

A career in ethical hacking can be attractive if you’re a professional who enjoys the pleasures of the computer world and lives on a challenge. You have the potential to put your skills to good use by breaking into computer systems and being paid well for it. It is not simple to break into the sector, as it is with many others, but if you put in the effort at the outset, you will be able to carve out a highly profitable career for yourself. Ethical hackers, on the other hand, are always on the right side of the law!

Let us learn how to become an ethical hacker after looking into the stages in the career of an ethical hacker.

What Are the Stages of a Career in Ethical Hacking?

If you want to continue a career as an ethical hacker, you’ll need to develop patience. You can’t expect to get a high-ranking job and great pay right away, but there is a lot of potentials to do both in a short period of time!

1. Starting Out

Many ethical hackers begin their careers by getting a bachelor’s degree in computer science. You can also get an A+ certification (CompTIA), which requires passing two additional tests. These exams assess a person’s understanding of computer components as well as their ability to remove and reassemble a computer.

You must have at least 500 hours of practical computing experience to take the test for the qualification. You may expect to make an average of $44,000 per year at this stage of your career. However, you must earn experience and obtain a Network+ or CCNA certification before you can advance in your career. The Network+ certification verifies fundamental network expertise, such as network management, maintenance, installation, and troubleshooting. The CCNA certification provides the same abilities and focuses on fundamental skills.

2. Network Support

After you’ve earned your certification, you can move on to the next level of your career, network support. Monitoring and updating, installing security programs, and testing for weaknesses are all things you’ll do here. Your goal should be to get a position as a network engineer after gaining expertise in the field of network security.

3. Network Engineer

You can expect to earn between $60,000 and $65,000 after getting experience in network support. Instead of only supporting networks, you’ll now be designing and planning them. From now on, you should focus on the security part of your journey to becoming an ethical hacker. Now is the time to work on security certification, such as Security+, CISSP, or TICSA, for example. The US Department of Defense has accepted the Security+ accreditation, which covers testing on important issues like access control, identity management, and cryptography.

The CISSP is a globally recognized security certification that verifies risk management, cloud computing, and application development skills. The TICSA qualification testing covers the same topics and is aimed at the same level of security knowledge. This experience, along with your accreditation, should be enough to help you take a job in information security.

4. Working in Information Security

This is a big step toward being an ethical hacker because you’re dealing with information security for the first time! This position pays an average of $69,000 per year. An information security analyst analyzes the security of a system or network, responds to security breaches, and works to implement security solutions. To obtain hands-on experience with some of the tools of the trade, you should focus on penetration testing for this profession.

You should aim for a Certified Ethical Hacker (CEH) certification from the International Council of Electronic Commerce Consultants at this point in your career (the EC Council). The instruction you receive will cover all you need to know to become a responsible and efficient hacker. You’ll be engaged in a hands-on environment where you’ll be guided through the process of hacking into a network and finding any security problems. You can begin selling yourself as a professional ethical hacker once you have obtained this certification.

Let us learn how to become an ethical hacker after understanding what are the expectations of the job.

Certifications For Ethical Hackers

While certifications do not determine aptitude or ability, they do assist in showing your knowledge and skill to others. A certification like Certified Ethical Hacker (CEH) is certainly beneficial, even if you don’t have much work experience. CEH is a neutral credential, and CEH-certified ethical hackers are in great demand in general. A CEH-certified ethical hacker earns roughly $88,000 per year, according to PayScale. Aside from CEH, there are a few more notable certifications that demonstrate your skill as an ethical hacker, such as:

• SANS GIAC certification
• Certified Vulnerability Assessor (CVA)
• Certified Professional Ethical Hacker (CPEN)
• Certified Penetration Testing Engineer (CPTE)

For now, let’s discuss CEH certification

How to become an Ethical Hacker: About CEH Exam

The CEH certification is a multiple-choice exam that evaluates your understanding of the penetration testing structure and the tools that are utilized inside it. It gives job seekers in the information security field a head start by ensuring that the certificate holder understands the fundamentals, such as:

• information gathering
• attacking computers or servers
• wireless attacks and social engineering

Objective of CEH

The following are the targets of the CEH exam:

• Inform the public that credentialed individuals meet or exceed the minimum standards
• Establish and govern minimum standards for credentialing professional information security specialists in ethical hacking measures
• Reinforce ethical hacking as a unique and self-regulating profession

Exam Overview

• Exam Name: EC-Council Certified Ethical Hacker
• Exam Duration: 240 Minutes
• Questions in Exam: 125 Questions
• Exam Type: Multiple Choice
• Passing Score(%): 70%
• Exam Registration: Pearson VUE OR ECC Exam Center

Eligibility Criteria for CEH

There are two ways to satisfy the CEH certification exam eligibility requirements:

1. Attend Official CEH Training: This can be in any format, e.g., instructor-led training, computer-based training (CBT), or live online training, as long as the program is approved by EC-Council.
2. Attempt without Official Training: In order to be considered for the EC-Council certification exam without attending official training, you must: Have two or more years of documented information security experienceRemit a non-refundable eligibility application fee of $100.00Submit completed CEH Exam Eligibility Form including verification from an employer upon approval EC-Council will email you a voucher number to register for the CEH exam

Steps to Becoming a CEH

The information we have already mentioned gives prospective candidates all the information required to plan their future CEH exams. We can summarize the steps needed to get the CEH certification as follows:

1. Attend a CEH training course from an accredited EC-Council training center.
2. If you have relevant work experience in cybersecurity, you can enroll directly in the exam. Applicants need to submit an eligibility form that costs a non-refundable $100 fee.

2.1. When selecting to enroll in the exam directly, it is advisable to read some resources about the CEH exam. The EC-Council provides many resources for self-study; there are also many books for preparing for the CEH exam. Check the free EC-Council resources page and the official store for courseware and other self-study materials.

3. After gaining confidence about your ability to pass the CEH exam, register for the CEH exam, take the test and receive a passing score.

CEH has launched a complement to the CEH certification; it is practical-based and is called CEH Practical. It is a 6-hour practical exam that involves 20 practical challenges. Taking CEH practical certification makes one eligible to continue on the path to getting the CEH Master certification. 

Role of an ethical hacker

Ethical hackers can work as freelance consultants, for a firm that specializes in simulated offensive cybersecurity services, or for a company’s website or apps as in-house employees. While all of these positions involve knowledge of current attack methods and tools, the in-house ethical hacker may be required to have in-depth knowledge of only one program or digital asset type.

While in-house red teams are still relatively new in the security business, one advantage they may bring is that they will have a better understanding of how their own systems and applications are built than an independent consultant. This secret information gives the red team an advantage, as long as they can keep their vision from becoming myopic. This advantage would take genuine attackers years to replicate. In-house teams are also regarded to be less expensive than hiring a consulting firm on a long-term basis.

Conversely, a benefit that an external ethical hacker may provide is a fresh set of eyes to identify vulnerabilities that may be overlooked by the internal team. Even organizations that employ an internal red team may occasionally contract an external ethical hacker to provide this fresh look at their defenses. 

Before beginning any offensive security actions, any external offensive security service provider should seek written consent from the client. The systems, networks, apps, and websites that will be included in the simulated attack should be specified in this permission. Do not broaden the scope of the service until you have received further explicit authorization.

There are white-box, black-box, and gray-box ethical hacker engagements, which follow the industry’s use of colors to distinguish between various cybersecurity responsibilities and functions. When a security professional is given as much information about the target system and application as possible, it is referred to as a white-box engagement. This enables the simulated assault to search widely and deeply for vulnerabilities that would take a genuinely bad actor a long time to find.

A black-box engagement, on the other hand, is one in which the ethical hacker is not given any insider information. This more closely resembles the circumstances of an actual attack and might give useful insight into how a true attack vector might look. A gray-box engagement, as the name implies, simulates an attack in which the attacker has already breached the perimeter and may have spent time inside the system or application.

Many companies utilize a combination of all three engagement styles, as well as in-house and external ethical hackers. This type of applied knowledge can provide you with the best picture of what defenses you need, but it’s also a lot more expensive.

Many additional security positions benefit from ethical hacker abilities and expertise. Network security analysts and network engineers require these abilities. Purple teams are in urgent need of offensive players. Understanding offensive strategies and tools is beneficial to application security developers. Security researchers, often known as bug hunters, rely heavily on their understanding of attack strategies. Many competent bug hunters have a thorough understanding of the application layer, as well as the network layer and other areas that can be abused.   

Start Learning Ethical Hacking on My Own

Recommended Books

1. Hacking for Dummies: The “for dummies” series of Wiley focuses on publishing beginner-friendly books on various topics. This book introduces the user to ethical hacking through concepts and tools. It is very useful for people who want to start learning ethical hacking but are not very comfortable with programming. This should however be understood that being an elite hacker is almost impossible without learning to program.

2. CEHv10 Study Guide by SYBEX: This book is aimed to aid the preparation of CEH(Certified Ethical Hacker), a popular certification course in ethical hacking. It explains the ethical hacking methodology and the phases of it. Each phase of ethical hacking is well explained with details of the concepts and practice on the tools.

3. Hacking, The Art of Exploitation :This book has been very popular in the community of white hat hackers for a long time. Probably because of the content it covers and the depth it goes into. The good thing about this book is that even if you are a novice with absolutely no knowledge about programming and networks, you can still benefit immensely. The book covers Basic Programming in C, Scripting with Bash, basics of memory management in computers, filesystems, overflow based vulnerabilities and their exploitation, basic networking, attacks on networks, writing shell-code, and cryptology.

Popular Online Courses

1. Udemy: These cybersecurity ethical hacking courses have been already taken by many people and their rating is quite good, so we are assuming these will be really useful for your self-learning.

• Learn Ethical Hacking From Scratch
• The Complete Ethical Hacking Course: Beginner to Advanced!
• Hacking in Practice: Certified Ethical Hacking MEGA Course
• Ethical Hacking with Hardware Gadgets
• CompTIA Pentest+ (Ethical Hacking) Course & Practice Exam

2. PentesterLab: PentesterLab is useful for beginners and advanced learners equally. Their beginner-friendly tutorials and labs are from highly successful ethical hackers and bug bounty hunters and they are also well known as instructors and mentors.

3. Pentester Academy: Pentester Academy is a platform of learning for beginners as well as seasoned hackers. They have courses and online labs for major vulnerabilities. Pentester Lab also has courses on programming, forensics, VoIP, DevOps Security, Red/Blue team, etc.

YouTube Channels

1. JackkTutorials: Provides hands-on introductory tutorials to almost all the important concepts, tools, and skills related to ethical hacking.

2. Thenewboston: This channel not only covers the basics of practical ethical hacking but also provides tutorials on programming, app development, graphic design, chemistry, databases, video editing, etc.

3. HackerSploit: Hackersploit is highly popular among bug bounty beginners. It has penetration testing tutorials, CTF walkthroughs, Bug bounty hunting tips, programming tutorials, malware analysis tutorials, steganography tutorials, etc.

LiveOverflow, Nahamsec, Bugcrowd, and HackerOne are also very good and worth checking out.

Websites and Blogs

These websites are useful for staying up to date with recent findings, for getting quick references, for understanding advanced concepts, and more.

1. PortSwigger’s Blog: Dafydd Stuttard is known as portswigger in the hacker community. He is a co-creator of the BurpSuite tool. His blog contains news about the Latest web application vulnerabilities, new features of BurpSuite, tips on how to use BurpSuite well, and his own findings as a bug bounty hunter.

2. TheHackerNews: A dedicated platform for the latest cybersecurity-related news. The Hacker News provides detailed information about the latest vulnerabilities, new developments in the cybersecurity domain, news related to cybercrimes, data breaches, hacktivism, etc.

3. HackerOne Hactivity(disclosed vulnerability reports): HackerOne is a bug bounty platform. Bug bounty programs may allow public disclosure of a vulnerability report after it is resolved. These reports can be used to understand how to look for vulnerabilities on a target, how to perform reconnaissance, how to approach interesting endpoints, how to exploit a vulnerability for maximum impact, and what kind of vulnerabilities are commonly found on a specific type of target.

The second step is practice: Practicing is very important because it will help you absorb the concepts you have learned. It will also help you gain confidence in your skills. Intentionally vulnerable virtual systems and CTFs are the best way to practice. bWApp, DVWA(Damn Vulnerable Web Application), Metasploitable are some of the best vulnerable VMs. The OWASP has put up an index of vulnerable virtual machines, which can be accessed here

Why Do You Want to be an Ethical Hacker?

The ethical hacking field is fascinating, but there are many other reasons behind taking it up as a career. Different people may have varied reasons for joining the ethical hacking niche. Some may choose it for the attractive salary packages, while others may like this job’s challenges. Before discussing the roadmap for ethical hacking, let us discuss the reasons to build a career in this niche.

Growing Opportunities

Regardless of the niche, every organization plans to switch to cloud networks. While some have already started working on the cloud, others will join the wagon. So, you can expect a sudden spike in the job opportunities in this industry. If you have the expertise level required by the organizations, you will never see a lack of options.

Attractive Salary

It is a high-paying profession where you can expect to get around 10 LPA at the beginning itself. As you gain more expertise and get hands-on experience in the field, your salary will keep growing. Furthermore, if you find a job in the defence sector or other public or government organizations, expect to get countless additional perks.

Challenging Industry

There will be new projects and challenges each day, so you will never feel bored or experience monotony at work! In addition, technology changes every day, so you will always have to be on your toes to keep up with the changing trends and deliver the best results. So, if you wish to be active and face new challenges each day, hacking is an ideal option.

Job Security

This industry will only see a rise in demand in the coming years, so choosing ethical hacking can secure your career. Companies will never stop using online networks for improved performance, and they will always need security professionals to help them keep the network safe.

There are countless other reasons to join ethical hacking as a career. All you need to do is take up a professional certification course and gain expertise in the field to deliver the best results to every organization. Moreover, as technology and its features are ever-changing, you should stay updated with the latest trends and keep up with the changing dynamics to prove your worth.

Benefits of Ethical Hacking

A highly desirable and interesting field of IT, ethical hacking is advantageous to both people and businesses. The individuals benefit from excellent salary packages and employment stability, and the businesses hire skilled personnel to handle the security requirements. The following are some advantages of ethical hacking:

• It helps keep the data and the network secured and safe from intruder attacks.
• You can identify the probable risks well on time and prepare your network to avoid the risks.
• Prepare an effective network strategy to help organizations make a smoother transition to cloud networks.
• A professional opting for the ethical hacking profession gets job security for a lifetime as this IT field will expectedly grow in the coming future.
• A qualified expert can expect to get attractive salary packages in this profession.

There are countless other benefits that ethical hacking offers. However, you only ripe all these perks if you are a qualified professional or have hired the best hacker with proven expertise.

What kinds of ethical hacker job roles are available?

While the term “ethical hacker” is a useful umbrella word, actual employment jobs in the field come in a variety of forms. Penetration testers are the most widely advertised employment, however many similar roles are referred to as “security analysts,” “information security consultants,” “network security professionals,” and other terms.

What Can You Expect as an Ethical Hacker?

When you start working as an ethical hacker, you’ll use all of your technical and security skills to try to break into the network security of the company or organization that hired you. The company will want a full analysis of your findings as well as recommendations to improve network security. This effort shields them from hacking attempts by people with malicious or criminal intents. The average salary you can expect as an ethical hacker is $71,000, with bonus payments often amounting to $15,000 – $20,000.

Ethical Hackers Salary

While codelivly is seeing annual salaries as high as $187,500 and as low as $22,500, the majority of Ethical Hacker salaries currently range between $90,500 (25th percentile) to $149,000 (75th percentile) with top earners (90th percentile) making $173,000 annually across the United States. The average pay range for an Ethical Hacker varies greatly (by as much as $58,500), which suggests there may be many opportunities for advancement and increased pay based on skill level, location, and years of experience.

Frequently Asked Question

• Explain what is Ethical Hacking?

Ethical Hacking is when a person is allowed to hack the system with the permission of the product owner to find weaknesses in a system and later fix them.

• What is the difference between an IP address and a Mac address?

IP address: Every device has an IP address allocated to it so that it can be found on the network. In other words, an IP address is similar to a postal address, in that anyone who knows your postal address can write to you.

MAC (Machine Access Control) address: Every network interface on every device is given a MAC address, which is a unique serial number. Only your postal carrier (network router) can identify your Mac address, and you may change it at any moment by getting a new mailbox (network card) and slapping your name (IP address) on it.

• What is the difference between Ethical Hacking and Cybersecurity?

Ethical Hacking is a technique used by ethical hackers to examine a situation and produce a report based on the information collected. Cyber Security is overseen by professionals whose job it is to protect the system from malicious actions and attacks.

• List out some of the common tools used by Ethical hackers?

• Meta Sploit
• Wire Shark
• NMAP
• John The Ripper
• Maltego

• What are the different types of hackers?

The types of hackers:

1. Black Hat Hackers or Crackers:  Illegally, they hack systems to gain unauthorized access and cause disruptions in operations or steal sensitive data.
2. White Hat Hackers or Ethical Hackers: These hackers hack systems and networks for the assessment of potential vulnerabilities or threats legally and with prior permission.
3. Grey Box Hackers: They assess the security weakness of a computer system or network without the owner’s permission but bring it to their attention later.

Aside from these three types, there are also other types of miscellaneous hackers.

• What can an ethical hacker do?

An ethical hacker is a computer system and networking expert who systematically attempts to breach a computer framework or network for the benefit of its owners in order to uncover security weaknesses that a malicious hacker could exploit.

• Why is Python utilized for hacking?

The most broadly utilized scripting language for Hackers is Python. Python has some very critical highlights that make it especially valuable for hacking, most importantly, it has some pre-assembled libraries that give some intense functionality.

• Explain what is Brute Force Hack?

Brute force hacking is a method of cracking passwords and gaining access to system and network resources that takes a long time and necessitates a hacker’s knowledge of JavaScripts. The utility “Hydra” can be used for this purpose.

• Explain what is SQL injection?

SQL is a weakness developed in the application code and is one of the techniques used to steal data from organizations. SQL injection occurs when you inject content into a SQL query string and result mode content into a SQL query string, and the outcome changes the syntax of your query in unexpected ways.

• What are the types of computer-based social engineering attacks? Explain what is Phishing?

Computer-based social engineering attacks are

• Phishing
• Baiting
• Online scams

Phishing is a technique that involves imitating a real system by sending fake e-mails, chats, or websites in order to steal information from the original website.

• Mention what are the types of password cracking techniques?

The types of password cracking technique include

• AttackBrute Forcing
• AttacksHybrid
• AttackSyllable
• AttackRule

• Explain what are the types of hacking stages?

The types of hacking stages are

• Gaining AccessEscalating
• PrivilegesExecuting
• ApplicationsHiding
• FilesCovering Tracks

• What is a firewall?

A firewall could be a device that allows/blocks traffic as per outlined set of rules. These are placed on the boundary of trusted and untrusted networks.

• What is data leakage? How will you detect and prevent it?

The term “data leak” knowledge leaves the organization in an unauthorized manner. Data will be leaked in a variety of methods, including emails, prints, computers going missing, illicit data transfers to public portals, portable drives, photos, and so on. There are a variety of controls that can be applied to ensure that information is not leaked. Some of these controls include limiting uploads on websites, following an internal encryption solution, limiting emails to the internal network, restricting the printing of confidential data, and so on.

• What are network protocols, and why are they necessary?

A network protocol is a set of rules that determines how data is transmitted between devices connected to the same network. It simply enables the communication between connected devices without any variations in internal structure, design, or procedures. In digital communications, network protocols are important.

Summary

• Programming skills are essential to becoming an effective hacker.
• Network skills are essential to becoming an effective hacker
• SQL skills are essential to becoming an effective hacker.
• Hacking tools are programs that simplify the process of identifying and exploiting weaknesses in computer systems.

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The network security engineer is responsible for the secure operation of the company’s networks and systems. They work with other engineers to design, implement, and maintain security measures for the protection of the organization’s data. If you’re interested in becoming a network security engineer, there are a few things you should know. In this blog post, we will explore what it takes to become a network security engineer and the skills you will need to succeed in this role.

What is Network Security Engineering?

The term “network security engineering” is used to describe the process of designing and implementing security measures for a computer network. The goal of network security engineering is to protect the confidentiality, integrity, and availability of network resources.

There are many different types of security measures that can be implemented in a network, including firewalls, intrusion detection systems, and encryption. Network security engineers must carefully select the appropriate security measures for their specific networks. They must also design the networks in such a way that the security measures are effective and do not interfere with the normal operation of the network.

Network security engineering is a challenging field that requires a strong understanding of both computer networking and security principles. Those interested in becoming network security engineers should consider pursuing a degree in computer science or a related field.

The Different types of Network Security Engineer

Network security engineers are responsible for the design, implementation, and maintenance of an organization’s network security infrastructure. They work closely with other members of the IT team to ensure that all systems are properly protected from external and internal threats.

There are three main types of network security engineer:

1. Infrastructure Security Engineer

Infrastructure security engineers are responsible for the physical security of an organization’s network. This includes ensuring that all hardware and software is properly configured and secured, and that all data is backed up in case of a disaster. They also work with other members of the IT team to develop plans for responding to incidents and emergencies.

2. Application Security Engineer

Application security engineers are responsible for the security of an organization’s applications. This includes developing and implementing policies and procedures for securing application data, as well as working with developers to create secure code. They also conduct regular audits of applications to ensure that they are meeting all security requirements.

3. Information Security Engineer

Information security engineers are responsible for the overall security of an organization’s information systems. This includes developing and implementing policies and procedures for protecting data, as well as working with other members of the IT team to ensure that all systems are properly secured. They also conduct regular audits of all systems to ensure that they are meeting all security requirements.

Pros and Cons of Network Security Engineer

As a network security engineer, you will be responsible for the security of an organization’s networks and data. This is a critical role that comes with a lot of responsibility. You will need to have a strong understanding of security principles and be able to design and implement secure network systems.

Pros:

-You will be in a position to make a difference in the security of an organization.
-You will gain a deep understanding of security principles.
-You will be able to design and implement secure network systems.

Cons:

-The role comes with a lot of responsibility.
-You will need to have a strong understanding of security principles.

What Education is needed to become a Network Security Engineer?

A network security engineer is responsible for the safety of an organization’s data and information systems. They design, implement, and monitor security measures to protect against cyber attacks.

To become a network security engineer, you will need at least a bachelor’s degree in computer science or a related field. Many employers also prefer candidates with experience in network administration or Information Technology (IT) security. Network security engineers typically have strong analytical and problem-solving skills, as well as experience with networking technologies and protocols.

Alternatives to becoming a Network Security Engineer

There are many alternative career paths to becoming a network security engineer. Some of these include becoming a network administrator, network analyst, or even a computer scientist. Each of these positions requires different skill sets and knowledge, but all are important in keeping networks secure.

Becoming a network administrator is a good option for those who want to work with networks on a daily basis. They are responsible for maintaining and troubleshooting network problems. They also often work with firewalls and other security devices.

A network analyst position is perfect for those who want to take on more of a research role. They often analyze data packets and look for trends that could indicate security breaches. They also develop new ways to protect networks from attacks.

Computer science is a great field for those interested in the inner workings of computers and networks. They often design algorithms and software that helps to secure information. Computer scientists may also work on developing new technologies that can be used in networking and security.

What skills does a network security engineer need?

A network security engineer needs a combination of technical and soft skills.

On the technical side, a network security engineer should have a well-rounded understanding of networking protocols and architecture, along with experience working with security tools and technologies. They should be able to identify and mitigate common security threats, and have a working knowledge of compliance standards like PCI DSS and HIPAA.

In terms of soft skills, communication is key for a network security engineer. They need to be able to explain complex technical concepts to non-technical staff, and work with other teams to ensure that everyone is on the same page when it comes to security best practices. Time management and problem-solving skills are also important, as network security engineers often have to deal with unexpected issues that arise.

How do I become a network security expert?

In order to become a network security expert, there are a few things you need to do. First, you need to have a strong understanding of networking concepts and how they work. Second, you need to be well-versed in security technologies and have a good understanding of how they work. Finally, you need to have experience working with network security systems and be able to troubleshoot them when needed.

If you have all of these things, then you should be well on your way to becoming a network security expert. However, if you find that you’re still having trouble, there are plenty of resources available online that can help you improve your skills. Just make sure that you’re always learning and growing as an engineer, and eventually you’ll reach the top of your field.

Is network security a good career?

As more and more businesses move their operations online, the need for qualified network security engineers is on the rise. A career in network security can be both challenging and rewarding, offering a chance to work with cutting-edge technology and defend against ever-evolving threats.

With the right skills and experience, network security engineers can command high salaries and enjoy job security in an industry that is only going to grow in importance in the years to come. If you’re interested in a career in network security, here’s what you need to know.

The role of a network security engineer is to design, implement, and maintain secure networking systems. This includes developing firewalls, intrusion detection/prevention systems, and encryption technologies, as well as overseeing the day-to-day operation of the network and responding to security incidents.

Network security engineers must have a solid understanding of networking concepts and protocols, as well as experience with common networking technologies such as routers, switches, and VPNs. They also need to be well-versed in cybersecurity principles and have hands-on experience with various security tools and solutions.

In addition to technical skills, network security engineers must also be able to effectively communicate with other members of the IT team and management. They need to be able to clearly articulate complex technical issues and present recommendations in non-technical terms.

How do I start network security engineering?

If you want to become a network security engineer, there are a few things you need to do. First, you need to get a bachelor’s degree in computer science or a related field. Then, you need to get experience working in network security. Finally, you need to get certified.

Getting a bachelor’s degree in computer science or a related field will give you the knowledge you need to become a network security engineer. You’ll learn about different types of attacks and how to defend against them. You’ll also learn about networking and how to design secure networks.

Experience is essential for becoming a network security engineer. You can get experience by working in IT or by doing internships at companies that deal with network security. Working in IT will give you hands-on experience with different types of networks and systems. Internships will give you the opportunity to work with experienced network security engineers and learn from them.

Certification is not required to become a network security engineer, but it can help you get hired. Certification shows that you have the skills and knowledge necessary to do the job. There are many different certifications available, so choose one that is relevant to your skills and interests.

Job outlook for Network Security Engineer

Employment of information security analysts is projected to grow 35 percent from 2021 to 2031, much faster than the average for all occupations.

About 19,500 openings for information security analysts are projected each year, on average, over the decade. Many of those openings are expected to result from the need to replace workers who transfer to different occupations or exit the labor force, such as to retire.

Is it worth learning?

In the United States, a Network Security engineer earns an average salary of 115,000$ per annum.

This salary differs depending on your years of experience. Let’s take the example of having ten years of IT experience; then, you can even earn more than 150,000$ per annum. You can get an idea about the salary on Glassdoor.

Resource To Learn Network Security

There are plenty of online resources to learn network security at your convenience, and the good news is it doesn’t cost a fortune.

Cybersecurity Fundamental

A specialization program to learn the basics, get familiar with various tools, and understand the concept of cloud security. This course is offered by IBM on Coursera.

Coursera is an online learning marketplace, so you may explore more courses and take what suits you. If you already know the basics, you may want to try this network security and database vulnerability program.

Complete Cyber Security

A good blend of network security practical courses. This is available on Udemy and aims to teach you everything you should know as a cybersecurity specialist with solid network security understanding.

Udemy is a marketplace, similar to Coursera. And, you can find plenty of other network security courses.

Udemy courses are accessible from any device, including Mobile, which means you can learn on the go.

edX

edX is very popular, and it provides online courses from over 140 institutions, including Harvard, MIT, NYU. At the time of writing, there are six relevant courses.

Pick the one that suits you the most.

Network Penetration Testing

A free way to learn network penetration testing for the beginner.

This video talks about many things, including the following.

• Passive OSINT
• Scanning Tools
• Enumeration
• Exploitation
• Shells
• Credential Stuffing
• Building an AD Lab
• LLMNR Poisoning
• NTLMv2 Cracking with Hashcat
• NTLM Relay
• Token Impersonation
• Pass the Hash
• PsExec

freeCodeCamp is a popular non-profitable YouTube channel for techies with 2M+ subscribers. It is an open-source community with a lot of free resources.

CBT Nuggets

CBT Nuggets got plenty of free videos on their YouTube channel. Their videos are informative, meaningful, and engaging. There are two playlists I would recommend.

Intro to Networking

Intro to Cybersecurity

Defensive Security

Fancy a book?

Defensive Security Handbook is one of the highly recommended books for Network Security. This book provides step-by-step instructions for dealing with a specific issue, which includes network infrastructure and password management, breaches and disasters, compliance, vulnerability scanning.

CompTIA Network +

CompTIA Network+ by Mike Meyers is one of the core books which makes you learn network security in-depth and helps you clear the CompTIA Network+ exam in the first attempt.

This book has clear instructions and real-world examples with hundreds of practice questions prepared by bestselling author Mike Meyers along with several training experts.

Cisco Blogs

For blog reading lover, security blogs by CISCO have plenty to offer. Many influencers and security experts share their knowledge so you can keep your knowledge up-to-date.

Cybrary

Cybrary is an online training and career development portal for Cybersecurity. You can get it started with the following two courses.

Cyber Network Security – covering core knowledge, necessary network applications, network design, protection components, firewall, etc.

Network Security – for beginner covering the basics of TCP/IP, port scanning, sniffing, firewall, honeypot, network design, etc.

CCNP

Cisco CCNP ENCOR (350-401): 2 Network Management, Security, and Automation by Kevin Wallace are one of the popular network security courses offered on LinkedIn Learning.

Cybersecurity Certification

This course is offered on Edureka. They provide live instructor-led training with 24×7 tech support.

There Cybersecurity Certification Course will help you in establishing a strong foundation towards your journey in the Cybersecurity domain. Below are the modules covered in this course:

• Security essentials
• Cryptography
• Computer networks and security
• Application security
• Data and endpoint security
• Identity and access management
• Cloud security
• Cyber attach phases
• Security process

CISSP

The Certified Information Systems Security Professional (CISSP) certification course offered by Simplilearn covers all areas of IT security so you can become a strong information security professional.

You are expected to learn the following.

• Security and risk management
• Asset security
• Security operation & engineering
• Security testing
• Network  &web security
• Software development security

Web designer Roadmap: Save So you’ve decided on a career as a web designer? After all, why not? Working in this creative field means you’ll be at the crossroads of cutting-edge technology, user experience design, and marketing trends. In the twenty-first century, web design is an interesting and in-demand profession. It can also pay well, but you’ll find that you’ll need a lot of job experience and a large customer portfolio to make a lot of money. The path to becoming a web designer can be winding and winding. As a result, you may be unsure where to find out information on the best and most effective path to your future goal. Should you really try to learn to code? Will watching many of the many instructive videos on web design that are widely available on the Internet be enough to enable you to get work as a web designer, or should you make a significant investment in a formal web design education? We’ve decided to put together a step-by-step tour of what you’ll need to do to become a web designer because there are so many inquiries about one of the trendiest careers and jobs in the 21^st century.

What is a Web Designer?

What is the role of a web designer? A web designer, in a nutshell, is a designer who generates digital elements for a website. A web designer’s purpose is to focus on a website’s general aesthetic and layout structure. A web designer may also work on the web content that appears on a site.

The look of the website includes the graphics, typeface, and colors used. The aesthetics of a website are grouped according to the layout structure. Less is always more when it comes to designing a beautiful site design. Excellent web design layouts are frequently simple to use and effectively represent a business.

Users like unique web design layouts because they are visually appealing. In order for a website to be user-friendly, successful webpage layouts are usually made to be simple to use. Users are unlikely to return to a website if the design is not user-friendly. Web designers strive for a good user interface (UI) on their sites since it eliminates the chance of user annoyance.

Recommended Roadmap

• Best Game Development Roadmap-2022
• Backend Developer Roadmap – A Beginners Guide

How to become a web designer

If you are interested in pursuing a career as a web designer, try following these steps:

1. Develop the necessary skills

To become a web designer, you need to be proficient in the technical aspects of building websites. While you can earn a college degree to pursue web design—such as an associate’s or bachelor’s degree in computer programming or graphic design—many web designers are self-taught. There are subject-specific courses available online that you can use to develop your skills. The most necessary skills for being a web designer include:

Visual design

Visual design is the art of selecting the right design principles to boost the look and enhance the feel of a website. Web Designers with advanced visual designing skills tend to do extremely well. Visual design is closely linked to user experience, only that it’s more related to the esthetic elements.

The most common tools used in visual designing are grid systems, spacing typography, color psychology, and type hierarchy.

UX

The user experience (UX) of a website can be described as the feeling visitors have during their end-to-end interaction with your website. UX is one of the most crucial factors in defining a website’s success, especially as design philosophy becomes increasingly user-focused.

A Web Designer who understands UX practices will always approach his designs from a user-first perspective. Conducting research is a crucial part of the UX process, and then using those insights to improve things like navigation, content, colors, and so on.

Design software

Like any other professional, you need the right tools to get the job done. You should definitely familiarize yourself with all the industry-standard design software. Tools like Adobe Photoshop, Illustrator, and Sketch are ones that almost all Designers use for important parts of their job like creating mockups, designing logos and images, and of course, modifying and enhancing photos.

A good Web Designer should learn how to use them.

Programming languages

Most web designers need at least some knowledge of basic programming languages, such as HTML, CSS, PHP, jQuery, JavaScript, and Flash. Freelancers need more coding knowledge, while web designers employed with an agency will likely work on a team that includes programmers and graphic designers.

Search engine optimization

Understanding SEO techniques will help a web designer create sites that appear prominently when users search for certain keywords or topics.

Web design theory

There are certain foundational principles for creating excellent websites, such as structure, user experience, and color theory. If you don’t attend college, you can still study this theory on your own by reading books on the subject or doing research online.

2. Learn Key Web Design Tools

Communication skills

Web designers need excellent communication skills to understand a client’s needs and want, correctly implement the client’s desires, and suggest their own ideas for a site.

Before you can get up and running as a Web Designer, you’ll need to learn an array of web design tools. Over time, better and more powerful web design software and tools have been developed and released, and as a result, Web Designers have never been in a better position to create beautiful and functional websites.

If you want to be a Web Designer, we recommend you learn the following tools:

WordPress

The longtime leader of all web design skills hasn’t lost any of its popularity. WordPress powers 27 percent of all the Internet’s websites and owns a stunning 76 percent market share around the world in CMS. WordPress boasts over 1,000 built-in themes and plugins that will allow you to easily build, edit, customize, enhance, and optimize websites.

InVision Studio

Even though WordPress is still overall most popular, InVision Studio is considered by many Web Designers to be the best overall tool for designing a website thanks to its array of features and rapid prototyping. With gestures and interactions like clicking, swiping, and hovering, you can also create custom transitions and animation.

Photoshop

Web Designers need to be able to make eye-catching and creative images — that’s why Photoshop is without a doubt the most crucial Adobe suite for Web Designers. Its limitless array of color options and different gradients give you everything you’ll need to put together dazzling patterns and prints.

Dreamweaver

Another part of the Adobe suite that Web Designers should get familiar with, Dreamweaver allows you to directly code your website design even if you’re not a programming pro. Ready-made design templates and other tools are especially helpful for newcomers who still want to put together an attractive, responsive design.

Sketch

Typically used mainly for user interface designs, Sketch is an essential tool for creating interfaces and prototyping. When you’re working with vector drawings and graphics, Sketch can make life a lot easier.

Google Web Designer

Google Web Designer will empower you to make compelling, visually stunning HTML5 content. To give your creative vision life, Google Web Designer allows you to use animation and interactive elements and integrates seamlessly with other Google products, including Google Ads and Google Drive.

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3. Work on Your Own Projects to Develop Your Web Design Skills

Now that you’ve stocked your web design tool belt, you’re ready to begin building websites. As you go, you’ll likely pick up a lot of skills you might not have expected. Some of these are technical skills, or “hard” skills, like programming in HTML, CSS, or even JavaScript. The best way to sharpen these skills is just to get started – the more sites you build, the better you’ll be.

There are also a number of user experience design skills that are useful for Web Designers to consider. Web Designers program the screens that users interact with – and they can be far more successful with a good grasp of user-centered design and responsive design.

As you build sites, try to interact with them the way a user might. Where are the pain points or navigational issues? How could the site be improved? Now you’re in the mindset of a UX Designer, and that’s going to be key to designing really solid web products.

4. Apply to Relevant Web Design Jobs

Begin your web design career by performing web design jobs to add to your site and portfolio and gain experience so you can earn more clients. You can search for job listings online if you want to work for an agency or use freelancing platforms to bid for opportunities.

There are many roles within the web design field. Someone with web design skills and the right mix of education and experience could ultimately be qualified for any of the following jobs:

• Web Designer
• Front-End Developer
• Front-End Designer
• UX/UI Designer
• Product Manager
• Visual Designer
• Interaction Designer
• Mobile Developer

5. Build your portfolio

As you gain clients, create websites, and become more experienced, keep an updated portfolio of your work and skills. Include examples of your work, positive client feedback, and a list of your design capabilities. You should have a version of the portfolio that you can email to potential clients and also keep the portfolio updated on your personal website.

6. Market yourself

If you want to be a freelance web designer, your success will depend partly on your ability to market yourself as well as your skills. While keeping your personal website and portfolio updated is a good start, find additional ways to make yourself known in the design community and to potential clients.

Use social media and professional profiles to increase your visibility. You can communicate with other web designers online to stay updated on industry news and job opportunities. Build positive relationships with your clients to ensure their repeat business and ask them to recommend you, and attend networking events to continue to develop your contacts.

7. Consider specializing

If you focus on a particular niche of website design, you may be able to earn recognition for your skills and gain more clients. Consider specializing in a certain area of web design, such as sites for e-commerce, mobile apps, or sites for specific types of businesses.

8. Stay updated

Web design is a constantly evolving field, as technology changes frequently, new design methods are created and consumer needs change. Keep learning, practicing, and developing your skills to remain current in your industry and prove to clients that you have the knowledge and skills necessary to build websites that fill their needs.

Web Design Resources You’ll Actually Want to Use & Bookmark  

Fonts

• Identify any font with What Font Is
• RightFont is a professional font manager app for Mac to help designers preview & manage font files
• Access a library of 990+ free licensed fonts at Google Fonts
• Get free commercial fonts at Font Squirrel
• DaFont has tons of downloadable fonts 
• One of the biggest collections of fonts

Icons

• Access packs of icon bundles for your next project at Round Icons
• Find beautifully crafter symbols for common actions and items at Material Design
• Find free icons, mockups and templates at Uplabs
• IconFinder is the leading search engine and market place for vector icons in SVG, PNG, CSH and AI format
• Flaticon is the largest search engine of free icons in the world 

Color Palettes

• At Contrast Grid, you can test many foreground and background color combos for compliance
• Find variations of a color with 0 to 255
• Create a color palette from a specific image with PaletteGenerator
• Coolors is a super fast color scheme generator
• Choose your favorite colors and get your Material Design palette generated and downloadable

Images

• Storyblocks has royalty-free stock images, video and audio
• Find over 1.7 million+ high quality free images and royalty free stock and images at Pixabay 
• Unsplash features beautiful, free images and photos you can download and use for any project
• Find stock images, photos, vectors videos and music at Shutterstock
• Explore 300,000+ free stock photos and royalty free images at Free Images

Image Optimizers

• Compress PNG for…wait for it….compressing PNG files
• Compress JPEG for compressing JPEG files (shocker!)
• Use Smush for compressing images inside of WordPress
• You can optimiz 4 different file types with Compressor
• Kraken adds more flexibility for optimizing images 

Lorem Ipsum Generators

• Cupcake Ipsum is a sugar-coated lorem ipsum generator
• Lorem Ipsum generator with origins and meaning
• Office Ipsum is a dummy copy for the corporate world
• Find all the ipsums in one place at Meet the Ipsums
• Learn about lorem ipsum and generate dummy text at Lipsum 

Favicon Generators

• Real Favicon Generator is a favicon generator for perfect icons on all browsers
• Use Favicon.cc to create and generate favicons including animated ones
• With Genfavicon you can turn any image into a favicon for your website
• Favic-o-Matic lets you upload an image or use the platform’s icons to create a favicon
• Xiconeditor is an easy, free tool for designing a favicon from scratch

Placeholder Images

• Place Holder is a free image placeholder service for web designers featuring billions of images
• Just as it sounds, the Dynamic Dummy Image Generator will generate dummy images with options including height, width, background color and more.
• Lorem Picsum is an easy to use, stylish placeholders using images from Unsplash
• Choose size, colors and text and generate images with a URL with Fake Images Please
• At Lorem Pixel use random images of a specific width and height in a variety of categories 

Browser Extensions

• What Font is the easiest way to identify fonts on web pages.
• CSS Peeper is a smart CSS viewer tailored for designers
• Detect the different technologies any website uses with Wappalyzer
• Web Developer is a toolbar with various helpful web developer tools 
• Grab a color from any website with the advanced eyedropper, color picker and color analyzer using ColorZilla 

Are Web Designers in Demand?

As it’s become more and more clear that the design and useability of a company’s web offerings are absolutely crucial to its bottom line, the demand for talented Web Designers has continued to rise at a feverish pace across every industry.

As a result, there’s no shortage of jobs available for skilled Web Designers — according to the BLS, employment of Web Developers is projected to grow 13 percent from 2018 to 2028, much faster outpacing the five percent average for all jobs.

Web Designers who can code are in a separate class – especially if they also have a bit of knowledge around the back end. A Web Designer who knows his or her way around programming languages like HTML, CSS, JavaScript, SQL, and Python will really stand out from the pack and also command a much higher salary. That’s why many working Web Designers wind up attending web development boot camps to round out their skill sets.

UX (User Experience) skills are also extremely valuable for a Web Designer to acquire. Mastering UX design fundamentals, design research and strategy, responsive design, user research, UI design, and how to provide visualizations would make you the total package as a Web Designer and very valuable addition to any company.

Salary for a Web Designer

The average salary for a Web Designer is $50,632 plus around $5,000 of other bonuses and perks, according to PayScale.

That number rises with experience. The average Senior Web Designer takes home $71,201 plus nearly $10,000 in bonuses, and salaries range up to $100,000.

Happy Coding…. ☺️

About us: Codelivly is a platform designed to help newbie developers to find the proper guide and connect to training from basics to advance.

Hey there, future game developer! Are you ready to jump into the thrilling world of game development? If you’ve got a love for video games and a burning desire to create your own, then you’re in the right place. This article is going to be your roadmap to a career in game development.

Why this guide, you might wonder? Well, navigating the vast landscape of game development can be as complex as a boss level fight in Dark Souls. From learning the basics, mastering different skills, to choosing the right development platform, there’s a lot to take in! That’s why we’ve compiled this comprehensive roadmap. Whether you’re a complete newbie or already have some experience under your belt, this article is here to help you level up your game development journey.

In the upcoming sections, we’ll cover the entire lifecycle of game development, the crucial roles in a game development team, the indispensable skills you’ll need, and, of course, the practical roadmap for your own path in game development. We’ll also tackle some common challenges, share success stories for inspiration, and glimpse into the future of game development.

Note to Beginners: These roadmaps cover everything that is there to learn for the paths listed below. Don’t feel overwhelmed, you don’t need to learn it all in the beginning if you are just getting started.

Purpose of these Roadmaps; The intention of these roadmaps is to give you a sense of the environment and to help you if you’re unsure what to learn next, not to urge you to choose what’s hot. You should gain a deeper grasp of why one tool is better suited for certain situations than the other, and keep in mind that hip and trendy does not always imply best suited for the job.

The Concept of Game Development

So, let’s start with the basics, shall we? What exactly is game development? If you’re picturing a lot of complex coding and cryptic algorithms, hold on a second. Yes, coding is a big part of it, but it’s not the only thing.

Game development is a bit like baking a cake (hear me out!). You’ve got different ingredients: a dash of creativity, a big dollop of programming, a sprinkle of art and animation, a smidge of sound design, and a generous helping of storytelling. When you mix all these together in just the right way, you get something truly delicious: a video game.

A game development roadmap (yep, that’s what this whole article is about!) is a step-by-step guide to help you gather these ingredients, learn how to mix them properly, and bake your own gaming masterpiece. It’s about making sure you’ve got the right recipe and skills to create an engaging, immersive experience that people will love to play.

So don’t worry if you’re new to this whole game development thing. We’re here to guide you through this roadmap, from understanding the basics to releasing your first game. Are you ready to start this journey? Great! Let’s get rolling.

Importance of Game Design and Development

So why should you care about game design and development? I mean, aren’t video games just about having fun and passing the time?

Well, not really. Games are so much more than that. Think about that game you can’t put down, that storyline that had you hooked, or that character you became emotionally invested in. Good game design can evoke strong feelings, create unforgettable experiences, and make us see things from a new perspective. It’s an art form in its own right, blending creativity, technology, and storytelling.

And let’s not forget about the industry side of things. Video games are big business. We’re talking billions of dollars big. As a game developer, you’re not just creating games for fun (although that’s a pretty great perk!). You’re contributing to a thriving, dynamic industry that’s growing every day.

Plus, game development is about problem-solving, creativity, and innovation. It challenges you to think outside the box, experiment with new ideas, and constantly learn new things. It’s like a never-ending adventure game in real life!

So, game design and development isn’t just about creating games. It’s about crafting experiences, telling stories, solving puzzles, and being a part of something really cool.

The Various Types of Games

When we say ‘games,’ what comes to your mind? Is it the adrenaline-fueled action of ‘Call of Duty’? The deep narrative of ‘The Witcher’? The pixelated charm of ‘Minecraft’? Or maybe, the addictive simplicity of ‘Candy Crush’? Here’s the beautiful thing about a game development roadmap: it can lead you to any or all of these types of games!

You see, the gaming world is a lot like a buffet, there’s a bit of everything for everyone. Games are not just limited to the blockbuster AAA titles that hit the shelves of your local game store. They span across various platforms and genres.

There are indie games that are pushing boundaries and making waves with unique gameplay and narratives. Mobile games that are hooking players with their accessibility and pick-up-and-play nature. Virtual and augmented reality games that are redefining immersion. Educational games making learning fun. The list goes on and on!

As a game developer, you have the freedom to choose your own path in this wide spectrum. Your game development roadmap isn’t limited to one kind of game or platform. Whether you want to create a heart-wrenching narrative experience, a mind-bending puzzle game, or a fast-paced mobile hit, the roadmap can guide you there.

It’s like being in an arcade with unlimited tokens. The only question is: which game will you choose to play (or in this case, create)? Dive into the next sections to find your answer!

The Game Development Lifecycle

OK, let’s get down to the nitty-gritty. The game development roadmap isn’t just about learning to code or create cool art. It’s about understanding how to take a game from an idea in your head to a playable product on someone’s screen. That’s where the game development lifecycle comes in.

Think of it like going on a road trip. You wouldn’t just jump in the car and start driving without knowing where you’re going, right? (Well, maybe you would, but that’s a different kind of adventure!) You’d plan out your route, figure out where to stop along the way, and maybe even decide where to grab a bite to eat.

Here’s how the game development lifecycle fits into our roadmap:

A. Pre-Production

Pre-production is the ‘planning your route’ stage. This is where you brainstorm ideas, decide on the game’s concept, create the game design document (like your road trip itinerary), and plan out your resources. It’s all about getting your ducks in a row before you hit the road.

B. Production

Now it’s time to hit the gas and start creating! Production is the stage where you develop the game based on the plan you’ve made. It’s about turning your ideas into reality. You’ll be programming, creating art, developing sound, and doing a whole lot more. Think of it as the driving part of your road trip.

C. Post-Production

You’ve reached your destination, but you’re not quite done. Post-production is like checking into a hotel and unpacking. It’s about testing your game, squashing any bugs, polishing up the details, and getting it ready for the players to enjoy. Then it’s time to launch your game and see it out in the world!

Remember, your game development roadmap isn’t set in stone. Like any good road trip, there can be detours, scenic routes, and unexpected pit stops. But that’s all part of the adventure, right? So buckle up, because our journey through game development is just getting started!

Sure, here’s a casual, conversational version of “Essential Skills for Game Development”, incorporating the keyword you mentioned:

Essential Skills for Game Development

Alright, by now you’re probably super pumped to start your journey on this game development roadmap. But before we hit the accelerator, let’s make sure you’ve got all the essentials packed. Just like you wouldn’t head out on a road trip without a map (or a GPS), snacks, and a great playlist, there are certain skills you’ll need on this game dev journey.

A. Programming Skills

Let’s start with the big one, the engine that’s going to power your journey along this game development roadmap – programming. “But I’m not a tech whiz,” you might be saying. No worries! We all start somewhere, right?

Programming is the heart and soul of making a game function. It’s like learning to drive for the first time. Yeah, it might seem intimidating with all those pedals, switches, and that darn parallel parking, but once you get the hang of it, you’ll be cruising down the game development highway in no time!

Depending on what type of game you want to create and which game engine you plan to use, the language you need to learn might vary. If you’re planning to work with Unity, you’ll need to learn C#. If Unreal Engine is your tool of choice, you’re looking at C++. And if you’re a beginner, starting with a more accessible language like Python can also be a great choice.

Remember, mastering a programming language isn’t a sprint, it’s more like a cross-country road trip. There will be bumps and detours along the way, but don’t let that discourage you. Even the best game developers had to start from scratch.

With the right resources, practice, and a dash of patience, you’ll see yourself coding like a pro, making vital pit stops along your game development roadmap.

B. Art and Animation Skills

Next up on our game development roadmap is something that truly makes your game pop – the art and animation. It’s like choosing the color and decorations for your car. It might seem not as essential as the engine (or programming in our case), but hey, who doesn’t like a ride that turns heads?

Let me tell you this, a game isn’t all about zeros and ones. It’s about the experience, and art plays a massive role in that. It’s what brings your characters to life, sets the mood of your scenes, and immerses the players in the world you’ve created.

You might be thinking, “I can’t even draw a stick figure. How am I going to create art for a game?” No worries! You don’t need to be Michelangelo to create art for your game. There are plenty of tools out there like Blender for 3D modeling, or Photoshop and GIMP for 2D art that can help you craft what you need.

And hey, maybe you do want to become the Michelangelo of game art. In that case, you might want to dive into learning about things like color theory, 3D modeling, character design, and animation.

Regardless of your current art skills, what’s important is the willingness to learn and improve as you journey along this game development roadmap. So, let’s get ready to make your game a visual treat!

C. Sound Design Skills

So, you’ve got your engine running and your ride is looking slick. What’s next on our game development roadmap? Well, what’s a road trip without a killer soundtrack? That’s right, we’re talking about sound design.

Now, sound design might seem like a bit of an afterthought compared to programming or art, but let’s imagine something for a second. Think about your favorite game, now strip away all its sound. No epic orchestral scores, no weapon clashes, no character voices. Feels quite empty, doesn’t it? That’s the power of sound design.

Whether it’s the satisfying ‘plink’ of a coin pick-up or the haunting melody that plays in a game’s dramatic climax, sound design adds depth and richness to the player’s experience. It’s the immersive layer that often goes unnoticed, but would be sorely missed if it wasn’t there.

“But I don’t know anything about creating sounds!” you might say. That’s alright! Like every other skill on this roadmap, sound design is something you can learn. There are fantastic resources and software out there, like Audacity or GarageBand, to get you started.

But remember, sound design isn’t just about making sounds. It’s about knowing when and how to use them to enhance your game’s atmosphere and narrative. So tune up your ears and let’s make some noise on this game development roadmap!

D. Narrative Skills

You know what makes a road trip memorable? It’s not just the destination or the cool spots you find along the way. It’s the stories you create and share. That’s exactly where we’re heading next on our game development roadmap – narrative skills.

In the gaming world, narrative is more than just a fancy word for ‘story’. It’s the backbone that supports your characters, your setting, your objectives, and even your game mechanics. It’s the voice that whispers, “just one more level” to your players late into the night.

“But I’m a developer, not a writer!” you might think. And you’re right. But as a game developer, you’re also a storyteller. Your medium might be code and pixels instead of words on a page, but your goal is the same – to captivate your audience and keep them coming back for more.

Does this mean you need to write a novel’s worth of lore for your game? Not necessarily (although if you want to, go for it!). It could be as simple as crafting a compelling backstory for your main character, or creating a mystery for players to unravel as they progress through your game.

Learning to tell a good story is an invaluable skill on your game development roadmap. It’s what turns a good game into a great one. So grab your pens (or your keyboards), and let’s start weaving some unforgettable tales!

E. Problem-Solving Skills

And here we are, nearing the end of our must-have skills for this game development roadmap. But there’s one more crucial item we can’t forget to pack – our problem-solving skills.

When you’re on a road trip, things aren’t always going to go smoothly. You might get lost, your tire might go flat, or that amazing diner you found online might be closed. What do you do? You problem-solve, adapt, and keep going. That, my friend, is a big part of game development.

You see, game development is kind of like solving a huge puzzle. You’ll constantly be faced with questions like, “How do I get this character to jump realistically?” or “Why isn’t this level loading correctly?” or even “How do I make this game fun?”

But don’t worry, this isn’t as daunting as it might seem. In fact, it’s what makes game development such an exciting journey. It’s about using your creativity, logic, and the skills you’ve been developing on your game development roadmap to find solutions and continually improve your game.

And guess what? Every time you solve a problem, you grow as a developer. So don’t shy away from the challenges that come your way. Embrace them, solve them, and keep moving forward on your game development journey!

These are the key skills you’ll need on your game development roadmap. Don’t worry if you’re not an expert in all of these areas right now. Like any good journey, it’s about learning and growing along the way. So, pack your bags, buckle up, and let’s hit the road!

Key Roles in a Game Development Team

So far, on our game development roadmap, we’ve talked a lot about the skills you’ll need to develop games. But unless you’re planning on being a one-person show (which is totally doable by the way), you’re going to be working with a team. And just like a band, every member of a game development team has a crucial role to play. Let’s meet the band, shall we?

A. Game Designers

Okay, so we’ve just met the band, and who’s up first on our game development roadmap? The game designers. These folks are like the dreamers and storytellers of the team. If the game development process was a road trip, they’re the ones holding the map and deciding which pit-stops to make.

Game designers start by sketching out the initial concept of the game. They think about the kind of world they want to create, the characters that inhabit that world, the stories that unfold there, and the rules that govern everything.

It’s like writing the first draft of a novel or the outline of a screenplay. You’re starting with a blank page (or more likely a blank screen) and filling it with your ideas. It’s about dreaming big and then figuring out how to turn those dreams into a game that people will want to play.

But being a game designer isn’t just about dreaming up ideas. It’s about understanding what makes a game fun and engaging. It’s about knowing your audience and what they’ll find interesting. And it’s about working closely with the rest of the team to bring your vision to life.

Remember, every game you’ve ever loved started as an idea in a game designer’s mind. If you’re an imaginative person who loves storytelling and games, game design could be the perfect pit-stop on your game development roadmap.

B. Programmers

Next up on our game development roadmap, we’ve got the programmers. If we’re sticking with our band metaphor, these are the folks riffing away on the guitar, making sure the rhythm of the game keeps flowing. On our road trip, they’re the ones under the hood of the car, ensuring everything is running smoothly.

Programmers are the ones who take all the amazing ideas from the game designers and turn them into a reality. They’re the ones who are writing the code that powers every element of the game, from the character movements, to the game physics, to the AI of the enemies. In essence, they’re bringing the game to life.

Programming might seem like a daunting task if you’re new to it, but trust me, it’s a skill that can be learned, just like playing an instrument. And the cool thing is, once you’ve learned one programming language, it becomes easier to learn others. It’s like learning to play one song on the guitar, and then realizing you can use the same chords to play a whole bunch of other songs.

Whether you’re going solo or part of a band (I mean, team), programming is an essential skill on your game development roadmap. So grab your pick (or your keyboard), and let’s rock this code!

C. Artists and Animators

Now, who’s ready for some showmanship? As we keep rolling on our game development roadmap, it’s time to meet the artists and animators, the lead vocalists of our band, if you will. They’re the ones who take the stage and bring a stunning visual presence to our game.

Artists and animators are vital to any game development team. They’re responsible for everything you see on the screen, from the lush landscapes and bustling cities to the quirky characters and fearsome enemies. Think about your favorite game – can you imagine it without its unique art style or the animations that bring its characters to life?

Whether it’s creating detailed 2D sprites or intricate 3D models, artists and animators transform the grey boxes and placeholder assets of early game development into the vibrant worlds that captivate players.

If you’ve always loved drawing, painting, or sculpting, then this role might be a dream come true for you. But even if you can’t draw a stick figure, don’t worry. There are countless tools and resources out there to help you develop your skills, and there’s always room for improvement.

D. Sound Designers

Let’s keep the good times rolling on our game development roadmap. Now, who’s next on our band’s lineup? Ah, yes, the sound designers, laying down the beats and setting the mood with their awesome sound effects and scores. These folks are the drummers of our band, bringing that extra layer of depth and realism to our game.

You see, sound design is so much more than just background noise in a game. It’s the creepy creak of a door in a horror game that sends shivers down your spine. It’s the triumphant fanfare when you finally defeat that tough boss. It’s the ambient noise that immerses you in a new, unexplored world. Sound designers breathe life into the game, adding another dimension to the player’s experience.

Whether you’re a musician, a tech geek, or just someone who appreciates good sound, there’s a place for you in sound design. It’s a field that combines creativity with technical skills, and the results can be truly stunning.

So, if you’ve got an ear for audio and a rhythm in your heart, sound design could be the perfect gig for you on this game development roadmap. Grab those drumsticks (or audio software), and let’s make some noise in the gaming world!

E. Game Testers

And now, as we’re driving further down our game development roadmap, we arrive at the unsung heroes of any game development team – the game testers. These are the folks that may not be center stage, but without them, the show wouldn’t go on. In our band analogy, they’re the roadies, checking the equipment and making sure everything works before the gig starts.

Game testers are critical in the game development process. They’re the first to play the game in its various stages of development, hunting for any bugs or glitches that could break the gameplay or ruin the player’s experience. They’re like the quality control team, making sure that the game is the best it can be before it hits the shelves.

But let me tell you, being a game tester isn’t just about playing games all day (as awesome as that sounds). It’s about being methodical, patient, and having a sharp eye for detail. It’s about playing the same level over and over again, trying every possible action, pushing the game to its limits to find any potential issues.

If you love playing games, have an analytical mind, and don’t mind a bit of repetition, then game testing could be a perfect stop on your game development roadmap. After all, there’s something pretty cool about being one of the first people to play a new game and helping to shape it into something great.

Remember, every role is essential in creating a successful game. And many people wear multiple hats, especially in smaller teams. So as you continue your journey on this game development roadmap, think about which role resonates with you the most. Who knows, you might end up being the lead singer and the guitarist!

So far, on our game development roadmap, we’ve talked a lot about the skills you’ll need to develop games. But unless you’re planning on being a one-person show (which is totally doable by the way), you’re going to be working with a team. And just like a band, every member of a game development team has a crucial role to play.

Popular Game Development Tools and Platforms

So, we’ve had a pretty rockin’ journey so far on our game development roadmap, haven’t we? We’ve met the band, understood their roles, and got a feel for the creative symphony that goes into making a game. But what about the instruments? What about the tools that our band members use to bring their vision to life? Let’s take a detour and check out the garage where all the magic happens.

When it comes to game development, there are a lot of tools and platforms out there to choose from, each with its strengths and weaknesses. The trick is to find the one that fits your needs and the type of game you want to create.

A. Unity

First up, we’ve got Unity. This is a real crowd-pleaser in the game development world, known for its versatility and ease of use. Whether you’re a seasoned professional or just starting your game development journey, Unity could be your go-to tool.

B. Unreal Engine

Next on the list, we have the Unreal Engine. This powerhouse is known for creating stunningly realistic 3D games. If you’re looking to create a visually impressive game, this is the tool for you.

C. Godot

Then there’s Godot, a free and open-source game engine that’s gaining a lot of fans. It’s particularly great for 2D games and smaller projects, and its friendly community is always ready to lend a hand.

D. GameMaker Studio

Last but not least, we’ve got GameMaker Studio. This is a great option if you’re new to game development and want to get your hands dirty without diving into complex coding.

Remember, the best tool is the one that works for you. Don’t be afraid to experiment and try out different platforms. After all, finding the right instrument is a crucial step on your game development roadmap. So grab your tool of choice, and let’s get back on the road to creating your first game!

Complete Guide For Game Development Roadmap

It’s easy to be intimidated by programming, and development. If you take the right steps, you can become a game developer. The information below will help you, whether you are looking to become a full-time developer or a freelancer.

1.  Choose a game engine

Before you can move forward, ensure that your computer or laptop has at least 4GB of RAM. 8 GB RAM is recommended.

A game engine is a software that is required for a game’s operation. Although you can make your game engine, you will need to have programming skills and plenty of time. However, there are many great free game engines available that you can use to create any kind of game you like. These game engines are used by the majority of game creators.

There are numerous gaming engines available on the market. Unreal Engine, Unity, Gamemaker, and many other game engines are very popular. Unity is the most user-friendly gaming engine for beginners. It has a vast community and allows you to make nearly any style of game.

Unreal is a popular game engine, but it can be difficult for an indie game developer to use. We will be using the unity game engine to create games. You don’t have to waste your time choosing a game engine. Unity allows you to make any kind of game for free.

2. Learn Unity

Unity Tech created Unity, a cross-platform game development engine. Unity Tech allows you to create 3D and 2D games. You can also create virtual reality or augmented reality games.

You can easily create your games with the many pre-built features.

Your first step is to master Unity. Once you are comfortable with it, you can move, rotate, place, and render components in the editor. This is quite simple, but you will need to be able to perform actions and movements using a programming language.

Unity software is free and can be downloaded on any computer. If you have an 8GB RAM pc/laptop, it runs smoothly. It lags if your 4GB ram computer/laptop is used.

3. Learn C# Programming Language

After learning the basics of Unity, you’ll need to master c# programming languages. This is used to create scripts within unity.

Microsoft’s programming language C# can be used to create web apps, mobile apps, and games.

It is much easier to learn than C++ or C. It is used in Unity for game development. It is essential to consider and acquire the fundamentals of the language. However, mastering it in 15 to 20 days can be tough.

You will need an IDE to learn and code c#. There is an official Microsoft IDE, which is the visual studio community version. It is free Forever and allows you to write C# code.

4. Learn about Game Design

You will be able to build logic in your games after learning unity and C#. However, your game won’t look great so learn how to design characters and environments for your games.

It’s a free and open-source 3D modeling program. It can be used to generate models for video games, as well as animations.

5. Learn Animation

Animation is also required in your game. Unity has an animation feature that you can use to create animations. Animation is difficult and can be challenging.

Third-party assets can be used in your game, but they are usually paid. If you don’t have the money to hire an animator or purchase any assets, you will need to learn animation. If you put in the effort, it is not difficult.

Game Development Roadmap

Well, folks, we’ve come a long way on our game development roadmap. We’ve met our band, checked out their instruments, and we’ve even jammed a little. Now, it’s time to get down to the nitty-gritty and lay down our roadmap to game development. Are you ready? Let’s go!

A. Learning the Basics

Okay, folks, here’s our first real step on this game development roadmap of ours. We’re talking about learning the basics. Just like you can’t write a rock anthem without knowing how to strum a few chords, you can’t start building a game without understanding the foundations of game development.

Now, the basics might sound simple, but they’re incredibly important. We’re talking about everything from understanding game genres and what makes a game fun to play, to getting your hands dirty with a little coding, art creation, and sound design.

Think of it like the first time you picked up a guitar or sat down at a drum kit. Sure, you might not have known how to play a full song yet, but you learned how to hold a pick, how to strike a drum, or how to play a simple melody. Those basic skills form the building blocks for everything else you’ll learn.

The same goes for game development. You start by learning a little bit of coding, maybe draw some simple shapes, create a few sound effects, or script a simple interaction. And as you learn, you’ll find you’re building a toolkit of skills that you can use to create your very own games.

B. Choosing a Development Platform

Next up on our game development roadmap, we’re faced with the question every band eventually asks: “What kind of music do we want to play?” Or in our case, “Which development platform should we choose?” This is a pretty important decision, because the platform you choose is going to be your stage for the foreseeable future.

Now, remember when I mentioned those different game development tools earlier? Unity, Unreal Engine, Godot, GameMaker Studio – these are just a few of the stages you could be performing on. Each one has its unique features and strengths, so you need to figure out which one best suits your style.

Choosing a development platform is a bit like choosing an instrument to play. Are you a guitar kind of person, or do you prefer the drums? Maybe you fancy yourself a keyboard player. In the same way, you might find that you prefer the user-friendly nature of Unity, or maybe you’re drawn to the robust capabilities of the Unreal Engine.

The important thing is to choose a platform that aligns with the type of game you want to create and your level of expertise. It’s okay to take some time to try out different platforms, mess around, and see what feels right.

Don’t worry if you feel overwhelmed by the options. Just remember, this is your tour, your performance. So, take your time, find your groove, and choose the stage that feels like home to you on this game development roadmap. Once you’ve done that, you’re ready to rock and roll!

C. Building Your First Simple Game

So, now that we’ve got our basics down and our stage set, what’s the next step on our game development roadmap? Well, it’s time to put on a show, my friends! It’s time to build your first simple game.

Now, hold on, before you get carried away imagining a grand epic saga with state-of-the-art graphics and a sweeping orchestral score, let’s slow it down a beat. Remember, every great band starts with a simple tune. And in the same way, your first game should be simple and doable.

Think of this as your first jam session. You’re not trying to create a chart-topper here. Instead, this is your opportunity to experiment and apply what you’ve learned so far. Maybe it’s a simple Pong clone, or a mini platformer with a single level. What matters is that you’re creating something from scratch, solving problems, and learning as you go.

You’ll run into bugs and hit some sour notes along the way, but that’s part of the process. With each issue you tackle, you’ll be building up your skills, and before you know it, you’ll have a playable game to call your own.

So go ahead, grab your tools, flex those creative muscles, and let’s make some gaming magic. You’re in charge here, and the world is your oyster. Just remember, the goal is to learn and grow as a developer, and there’s no better way to do that than by creating something of your own.

So, ready to rock this game development roadmap? Let’s get started on building your first game!

D. Building Your Skills through Projects and Practice

Alright, now we’re getting into the groove! You’ve laid down your first track, and that’s a huge step on this game development roadmap. But we’re far from done. Our next step? Building your skills through projects and practice.

Think of it like this: You’ve just played your first gig, and it was a blast. Now, how do you get better? How do you go from garage band to headlining act? Practice, my friend, and lots of it.

In the world of game development, that means taking on more projects. Each new game you create is like a new song, a new opportunity to try something different, challenge yourself, and learn something new.

Maybe you tackle a new genre, or try out a mechanic you’ve never used before. Maybe you push yourself to create more complex art, more intricate sound design, or a more sophisticated narrative. With each project, you’re flexing your game development muscles, learning new techniques, and improving your skills.

And remember, it’s not just about the end result. The process itself, the act of creating and problem-solving, is where the real learning happens. It’s in the late-night coding sessions, the bug-fixing marathons, the sudden “aha!” moments. That’s where you’re growing as a game developer.

So keep building, keep creating, and keep practicing. That’s how you’ll master your craft and keep progressing on your game development roadmap. And who knows? Your next project could be your breakout hit!

E. Collaborating with a Team

We’ve come a long way on our game development roadmap, haven’t we? We’ve learned the basics, chosen our platform, built our first game, and started practicing our craft. Now, it’s time for a new challenge: Collaborating with a team.

Here’s the thing, guys – game development isn’t a solo act. Sure, there are one-man bands out there, but most of the time, it’s a team effort. Think about it – you’ve got artists, programmers, sound designers, game designers, all working together to create one awesome game. It’s like being in a band, where each member brings their unique skills and talents to the table.

Collaborating with a team can be a game-changer (pun intended). Suddenly, you’re not just bouncing ideas off your own head – you’re brainstorming with other creative minds, solving problems together, and learning from each other. It can be a challenge to sync up and harmonize at first, but when you do, it’s nothing short of magic.

It’s all about communication, understanding each other’s roles, and respecting each other’s contributions. So, get out there, find some like-minded folks, and start jamming. Who knows? You might just form the next superstar game development band.

F. Releasing and Marketing a Game

Well, folks, we’ve hit the final note on our game development roadmap. We’ve gone through learning the basics, choosing a platform, building games, honing our skills, and working as a team. Now comes the grand finale: Releasing and marketing a game.

This part’s a lot like releasing a hit single. You’ve been practicing in the garage, and now it’s time to get your sound out there. You’re ready to release your game into the wild, for players around the world to enjoy. It’s thrilling, isn’t it? But it’s also a whole new challenge.

Just like a newly released song, your game needs to reach the right ears—or, in this case, the right eyes. This is where marketing comes in. You could have the greatest game in the world, but if no one knows about it, it’s like a great song that no one’s ever heard. So, get ready to wear a new hat – you’re not just a game developer now, you’re a promoter too.

From social media promotions to game trailers, from reaching out to influencers to attending gaming conventions, marketing your game can take many forms. It’s all about creating a buzz, generating interest, and getting your game in front of potential players.

Remember, releasing your game is not the end of your journey. In fact, it’s just a new beginning. And as you venture into marketing and beyond, know that you’re following in the footsteps of all the greats who’ve come before you on this game development roadmap.

And there you have it! That’s our game development roadmap. Just remember, this is your journey. Feel free to take detours, explore new paths, and find your rhythm. The world of game development is vast and exciting, so buckle up and enjoy the ride!

Sure, here’s a casual, conversational version of “Challenges in Game Development and Solutions”, incorporating the keyword you mentioned:

Challenges in Game Development and Solutions

Alright, we’ve been jamming on this game development roadmap for a while now, haven’t we? And it’s been a fun ride so far. But, just like in any journey, there are going to be some bumps along the way.

Game development is an epic quest, my friends, and like any quest, it comes with its share of dragons to slay. Maybe it’s hitting a wall with your coding, struggling to get your visuals just right, or figuring out how to get your game in front of the right audience. And let’s not forget the biggest dragon of them all: the ever-looming deadlines.

The good news is, every challenge is also an opportunity in disguise. Each hurdle is a chance to learn, grow, and become a better game developer.

When you hit a coding snag, for example, it’s a chance to expand your programming knowledge. A visual challenge can push you to learn new art techniques. And figuring out how to market your game? That’s all part of becoming a well-rounded game developer.

As for those looming deadlines, they can actually be your secret weapon. They can push you to prioritize, stay focused, and get stuff done. And nothing beats the feeling of finally crossing that finish line.

So, yes, there will be challenges on this game development roadmap. But with every challenge you conquer, you’re one step closer to becoming the game developer you want to be.

And remember, you’re not in this alone. There’s a whole community of game developers out there, facing the same challenges and ready to lend a helping hand.

Sure, here’s a casual, conversational version of “Case Studies of Successful Game Development Projects”:

Case Studies of Successful Game Development Projects

We’ve had quite the journey on our game development roadmap, right? We’ve learned the ropes, built our first games, worked with teams, faced down challenges, and even released our creations to the world. Now, it’s time for a little story time, my friends.

You see, nothing inspires me more than hearing about game developers who started from scratch, faced down their dragons, and created some truly legendary games. It’s like hearing about a band that started in a garage and ended up selling out stadiums. And guess what? There are plenty of those stories in the world of game development.

Take the story of Mojang and “Minecraft” for example. Here was a small indie team that created a sandbox game and ended up making one of the biggest hits in gaming history. Or how about the tale of Toby Fox and “Undertale”, a game he developed almost entirely on his own, which went on to become a beloved cult classic?

Then there’s “Among Us”, developed by a small team at InnerSloth. It was pretty quiet when it first launched, but after two years, it suddenly blew up and became one of the most popular games of 2020.

These aren’t just success stories, they’re case studies in what’s possible in game development. They’re a reminder that with passion, perseverance, and a whole lot of creativity, anything is possible.

So, as we wrap up our journey down this game development roadmap, let’s take some time to celebrate and learn from these success stories. After all, who knows? The next big success story could be yours.

Conclusion

And there we have it, folks! We’ve reached the end of our grand tour on this game development roadmap. It’s been quite the journey, hasn’t it? From getting our hands dirty with the basics, to building and releasing our own games, we’ve covered a lot of ground.

We’ve talked about the importance of design and development, the different types of games, and the key skills you need to become a rockstar game developer. We’ve taken a deep dive into the various roles in a game development team and the tools and platforms that can help you bring your game ideas to life.

We’ve also talked about the challenges you might face along the way, and how to tackle them head-on. And we’ve taken some time to get inspired by the success stories of other game developers who’ve made it big.

At the end of the day, game development is an epic quest. It’s about bringing your game ideas to life, facing down challenges, and never stop learning and growing. It’s about creating something that players around the world can enjoy and get lost in. And most of all, it’s about having fun.

Remember, this game development roadmap is just a guide. Your journey as a game developer is your own, and it’s up to you to make the most of it. So, get out there, start creating, and who knows? Maybe you’ll be the next big thing in the gaming world.

Thank you for joining me on this adventure. Now, it’s time for you to start your own. Rock on, my friends!

Best Game Development Bootcamps and Courses

Sure, here’s an overview of some of the best game development bootcamps and courses available as of my last training data cut-off in September 2021. Keep in mind that new programs might have come up since then, so always research the most recent options.

I. Game Development Bootcamps

1. Make School: Make School offers a Bachelor’s degree in Applied Computer Science, which includes modules on game development. The program emphasizes practical, project-based learning over traditional lectures.
2. Ironhack: Ironhack offers a nine-week full-time or a 24-week part-time UX/UI design bootcamp. While not specifically focused on game development, UX/UI design skills are critical in creating engaging game interfaces.

II. Online Game Development Courses

1. Coursera “Game Design and Development” Specialization by University of Michigan: This 5-course program covers everything from an introduction to game development to more complex topics like character design and game testing.
2. Udemy “Unreal Engine C++ Developer Course”: This course is great for those interested in using Unreal Engine for game development. It covers C++, the main programming language for this engine, and teaches practical, hands-on skills through creating games.
3. “Introduction to Game Development” by Harvard University (edX): This course covers the basics of game development, game design, and game modification. It utilizes the Lua programming language and the LÖVE2D game engine.
4. Unity Learn Premium: Unity offers a comprehensive learning platform where you can access numerous courses and tutorials related to game development using the Unity engine. It includes beginner to advanced level modules.

Remember, while bootcamps and courses provide great foundational knowledge and skills, hands-on experience is also invaluable in game development. Consider working on your own game projects or collaborating with others to supplement your learning.

Why should you learn Game Development?

If you enjoy gaming and are creative, game creation is a terrific career opportunity. If you study game development, it will teach you fundamental programming languages and other tech skills.

This can be a great career option with multiple paths. You can either start your own business or apply for a job at one of the top gaming companies.

Game Developer Salary and Outlook

The Bureau of Labor Statistics (BLS) classifies game developers as applications software developers, with a median annual income of roughly $101,800. The lowest 10% of game developers earn around $59,900 per year, while the top 10% earn over $160,100. Benefits for full-time game developers include medical, vision, and dental care, as well as 401(k) and profit-sharing plans, as well as paid holidays and vacations.

According to the Bureau of Labor Statistics, the game development occupation will increase at a rapid rate of 31% from 2016 to 2026. While there are changes in console and computer game production, the demand for mobile games is particularly strong. Game developers with knowledge of a variety of game development tools and programming languages have a greater chance of success.

Frequently Asked Questions

Q1: How long does it take to develop a game?
A: The time it takes to develop a game can greatly vary depending on its complexity, the size of the team, the experience of the developers, and many other factors. It could take anywhere from a few weeks for simple mobile games to several years for AAA titles.

Q2: What programming language is best for game development?
A: Several programming languages are commonly used in game development. C++ is often used for AAA games, C# is widely used with the Unity game engine, and JavaScript or TypeScript can be used for HTML5 games. The best language depends on the specific needs of the game and the platform it will run on.

Q3: Do I need a degree to become a game developer?
A: While a degree in computer science or a related field can be beneficial, it’s not a strict requirement to become a game developer. Many successful game developers are self-taught or have learned through bootcamps or online courses. Practical experience, a solid portfolio of projects, and a deep understanding of games are key.

Q4: Can I develop a game by myself?
A: Yes, many successful games have been developed by solo developers. However, keep in mind that developing a game by yourself means you’ll need to cover all areas of game development, including game design, programming, art, sound, and testing.

Q5: How much does it cost to develop a game?
A: The cost of developing a game can vary enormously depending on its complexity, the size of the team, the resources used, and many other factors. It could be almost nothing for a simple game developed by a solo developer, or it could go up to millions for large, AAA titles.

Q6: How do I start learning game development?
A: You can start learning game development by picking up relevant skills like programming, game design, and graphics. Online courses, tutorials, and bootcamps can be good resources. Starting small, like modifying existing games or building simple ones, can help you gradually build up your skills.

Q7: Can I make money from game development?
A: Yes, there are several ways to make money from game development, such as selling your games, offering in-game purchases, earning ad revenue, or working for a game development company. However, it’s important to note that making profitable games is challenging and requires a combination of technical skills, creativity, and a good understanding of what gamers want.

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About us: Codelivly is a platform designed to help newbie developers to find the proper guide and connect to training from basics to advance.

Hello there! Welcome to this journey through the vast and intriguing world of Machine Learning (ML).

I guess you’ve heard of machine learning, right? You’re not alone if the concept feels a bit like science fiction. It seems to pop up in every other conversation these days, doesn’t it? And for a good reason! Machine learning is a critical component of the modern data-driven world. It’s behind the scenes, powering everything from your email’s spam filter to the recommendations you get on your Netflix queue.

But what is Machine Learning exactly? At its core, machine learning is a method of data analysis. It’s all about the creation of algorithms that can learn from and make decisions or predictions based on data. Think of it as a system that can learn from experience.

The field of machine learning isn’t new. It has its roots back in the mid-20th century, but it’s only recently that we’ve had the computational resources to put these ideas into practice at a large scale. It’s a rich field with a history full of fascinating ideas and breakthroughs.

So, why is it important? Well, machine learning allows us to deal with complex environments and vast amounts of data, making sense of it all without being explicitly programmed for the task. It enables computers to find hidden insights without being explicitly programmed where to look. In essence, it’s a way to get computers to be smarter and more flexible, which is pretty cool, right?

In the following sections, we’ll dive deep into the world of machine learning. You’ll learn about the different types of machine learning, the process involved, common algorithms, how to choose the right algorithm, practical aspects, and even advanced topics. We’ll also take a look at its application in various sectors and what the future holds for ML. Are you ready? Let’s get started!

What is machine learning?

Machine Learning Roadmap : Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.

Recommendation engines are a common use case for machine learning. Other popular uses include fraud detection, spam filtering, malware threat detection, business process automation (BPA) and predictive maintenance.

Basic Concepts in Machine Learning

All aboard the machine learning roadmap train, my friend! This next stop is all about the basic concepts in machine learning.

First and foremost, let’s talk about the types of machine learning. There are a few different ways we can categorize machine learning algorithms, but I’ll focus on the most common types: supervised learning, unsupervised learning, reinforcement learning, semi-supervised learning, and self-supervised learning.

1. Supervised learning is like learning with a tutor. You provide the algorithm with input data and the corresponding output. The algorithm’s job is to figure out a rule or pattern linking the input to the output. Once it’s learned this rule, it can apply it to new, unseen data. Isn’t that neat?
2. Unsupervised learning is a bit like learning to paint by experimenting. You’re given paints and brushes, but no instructions. In ML, this means the algorithm is given input data but no corresponding output data. It must find patterns and relationships within the data all on its own. It’s a challenging task, but it can lead to some powerful insights!
3. Reinforcement learning is a bit like learning to play a video game. The algorithm – or ‘agent’ – learns by interacting with an environment, receiving rewards for good actions and penalties for bad ones. Over time, it figures out the best strategy, or ‘policy,’ to get the most rewards. It’s a lot like training a dog, but instead of “sit” or “stay,” we’re teaching it to play chess or navigate a maze.
4. Semi-supervised learning is a middle ground between supervised and unsupervised learning. The algorithm is given some data with outputs (like in supervised learning) and some without (like in unsupervised learning). It can be a pretty handy technique when labeled data (data with outputs) is hard to come by.
5. Self-supervised learning is another exciting area. It’s a form of supervised learning where labels are generated from the input data itself. It’s like having a book where questions and their answers are on the same page. The challenge is knowing how to ask the right questions.

Alongside these types of learning, there are some key terminologies that you’ll come across on the machine learning roadmap. Terms like ‘features’ (the characteristics or attributes of your data), ‘training’ (the process of teaching your algorithm), ‘model’ (the representation of what your algorithm has learned), and many more. Don’t worry, though; we’ll get into all these as we continue down this road!

Stay with me, and you’ll become a machine learning maestro in no time!

Detailed Overview of Machine Learning Process

And here we are, the next stop on our machine learning roadmap: the machine learning process itself. Let’s dive right in!

1. Data Collection This is where everything starts. Just like a chef needs ingredients to cook a meal, machine learning algorithms need data to learn. This could be anything from spreadsheets full of sales data to thousands of pictures of cats and dogs. What data you collect depends on what problem you’re trying to solve. It’s a big wide data world out there!
2. Data Preprocessing Once we’ve got our data, we need to get it ready for our machine learning algorithms. This stage, called data preprocessing, can involve cleaning up messy data, filling in missing values, converting text data into numbers that an algorithm can handle, and more. This is like prepping your ingredients before you start cooking – it makes everything else go more smoothly.
3. Model Building Now that our data is ready, we can start building our machine learning model. This is where we choose what type of machine learning we’ll use (remember our friends, supervised and unsupervised learning?) and set up the structure of our algorithm. It’s like choosing a recipe and getting all your pots and pans ready.
4. Model Training With our model built, it’s time to train it on our preprocessed data. The algorithm sifts through the data, learning patterns and rules. In supervised learning, it uses the outputs in the data to guide its learning. It’s like cooking the meal – the stove (our algorithm) is applying heat (learning) to the ingredients (our data) to make a delicious dinner (a trained model).
5. Model Evaluation After our model is trained, we need to see how well it’s learned. This evaluation phase is where we test our model on unseen data. If it does well, fantastic! If it doesn’t, we might need to revisit our data, tweak our model, or even collect more data. It’s the taste-test of our machine learning meal.
6. Model Deployment Once we’re happy with our model’s performance, it’s time to put it to work – this is known as deployment. Whether it’s predicting stock prices, filtering spam emails, or driving autonomous vehicles, our model is ready to make real-world predictions!

And there you have it, a bird’s eye view of the machine learning process! It’s like a thrilling rollercoaster ride, right? Stay tuned on this machine learning roadmap, as up next, we’ll delve into the various algorithms that make all of this possible!

Common Algorithms in Machine Learning

Alright, pals! As we continue our journey on this machine learning roadmap, we’re now going to visit the famous landmarks known as machine learning algorithms. These are the bread and butter of any machine learning project.

1. Regression Algorithms Ever tried predicting your monthly expenses based on your past spending? That’s where regression algorithms come in handy! These algorithms model the relationship between features and target variables and are great for predicting numeric outcomes. Examples include linear regression, logistic regression, and polynomial regression. So next time you’re budgeting, you know which tool to reach for!
2. Classification Algorithms Have you ever tried to sort your emails into ‘spam’ and ‘not spam’? That’s a job for classification algorithms. These algorithms predict discrete outcomes – basically, they put things into categories. Examples include decision trees, k-nearest neighbors (K-NN), and support vector machines (SVM). Thanks to these guys, your inbox is a little less cluttered!
3. Clustering Algorithms Imagine you have a bunch of random songs, and you want to organize them into playlists. You could use a clustering algorithm for that! These algorithms group similar data points together. They’re a type of unsupervised learning and are super useful when you don’t have labels for your data. Examples include k-means clustering, hierarchical clustering, and DBSCAN. Let the good tunes roll!
4. Dimensionality Reduction Algorithms Ever feel overwhelmed by having too much information? Dimensionality reduction algorithms can help with that! They simplify your data without losing too much information, kind of like compressing a file. They’re super handy when dealing with high-dimensional data. Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) are a couple of examples. Less is more, right?
5. Ensemble Methods Can’t decide which movie to watch tonight? Ask all your friends and go with the most popular choice! That’s the idea behind ensemble methods. These algorithms combine several machine learning models to make better predictions. Examples include Random Forest, Bagging, and Boosting. Who knew machine learning could be so democratic?

And that’s a brief introduction to the common algorithms in machine learning! Remember, choosing the right one depends on your specific problem, the data you’re working with, and the insights you’re after. Don’t worry, though; you’ll get the hang of it as you continue along this machine learning roadmap. Keep moving; the journey is just getting exciting!

Choosing the Right Machine Learning Algorithm

So, as we move forward on this machine learning roadmap, you might be wondering, “With so many algorithms, how do I know which one to choose?” Fear not, my friend! I’m here to help.

1. Problem Understanding You know how you wouldn’t use a hammer to fix a watch? The same goes for machine learning algorithms. You have to understand the problem you’re trying to solve. Are you predicting numerical values? Then regression might be your thing. Categorizing emails? Sounds like a classification problem. The right tool for the right job!
2. Dataset Characteristics Next up, consider your data. If it’s labeled, you might opt for supervised learning. If not, unsupervised methods could be the way to go. The size and quality of your data matter too. Some algorithms handle large datasets better than others. Some are more robust to noise and missing values. Know your data, and you’re halfway there!
3. Algorithm Strengths and Weaknesses Each algorithm has its own strengths and weaknesses, just like superheroes! For example, decision trees are simple to understand and use, but they can easily overfit. SVMs are powerful and flexible but can be memory-intensive and tricky to tune. It’s essential to weigh the pros and cons based on your specific needs.
4. Model Performance Metrics Last but not least, you’ll want to consider how to measure success. This could be accuracy, precision, recall, F1 score, or something else entirely. The metric you choose will depend on your problem and what trade-offs you’re willing to make. Once you decide, you can compare different algorithms and see which one performs best!

Choosing the right algorithm might seem daunting, but don’t worry. It’s part science, part art, and part just giving things a try. As you continue on this machine learning roadmap, you’ll get a feel for what works and what doesn’t. Remember, machine learning is a journey, and you’re doing fantastic!

Practical Aspects of Machine Learning

Alright, we’re back on the move on our machine learning roadmap. Next up: the practical aspects of machine learning. We’ve talked about the what and the how, but now let’s get down to the nitty-gritty of actually putting it all into action.

1. Data Quality Before you even start with machine learning, you need good data. Garbage in, garbage out, as they say. So, it’s super important to clean up your data, deal with missing values, remove duplicates, and correct errors. No shortcuts here, I promise it’s worth it!
2. Feature Selection and Engineering Features are like the ingredients in your machine learning recipe. Some add lots of flavor; others might not do much. Feature selection is about finding the most useful features, and feature engineering is about creating new ones from the data you have. It’s like figuring out that adding a little cinnamon takes your apple pie to the next level!
3. Algorithm Tuning Once you have your algorithm set up, it’s not just a set-it-and-forget-it kind of thing. Most algorithms have parameters that need to be adjusted (like the temperature and time when you’re baking). This is known as hyperparameter tuning. The right settings can make a big difference in your results.
4. Model Validation You wouldn’t serve a meal without tasting it first, right? The same goes for your machine learning model. Model validation techniques like cross-validation help ensure that your model performs well, not just on your training data but on new, unseen data as well.
5. Iterative Process Lastly, remember that machine learning is an iterative process. You’ll probably need to go through the steps more than once, refining each time, before you’re happy with your model. It’s like experimenting with a recipe until you get it just right. It takes patience, but it’s part of the fun!

And there you have it: the practical aspects of machine learning! The hands-on, getting-your-hands-dirty part of the process. It’s not always glamorous, but it’s where the magic happens on the machine learning roadmap.

Advanced Topics in Machine Learning

Alright, my friend, fasten your seatbelt because we’re about to speed up on this machine learning roadmap. We’ve laid the foundation, gotten our hands dirty with the practicalities, and now it’s time to venture into the more advanced territories of machine learning. Let’s hit the road!

1. Deep Learning Deep Learning is like the cool cousin of traditional machine learning. It’s all about neural networks with many layers (hence the ‘deep’ part). Deep learning shines when dealing with large amounts of unstructured data like images, audio, and text. So, if you want your machine to recognize cats in pictures or understand human speech, deep learning is your jam.
2. Reinforcement Learning We’ve touched on this before, but reinforcement learning deserves another mention here. It’s about learning from trial and error, receiving rewards for good choices and penalties for bad ones. It’s how AlphaGo, the AI by Google, managed to beat the world champion at the game of Go! Fascinating stuff, if you ask me.
3. Natural Language Processing (NLP) NLP is all about helping machines understand and generate human language. From translating languages to detecting sentiment in social media posts, NLP is a huge field with lots of exciting applications. It’s what makes your virtual assistant understand your commands.
4. Transfer Learning With transfer learning, you can take a pre-trained model and fine-tune it for a slightly different task. It’s like learning to play the guitar and then switching to the ukulele. You don’t have to start from scratch. It’s a massive time saver, especially when dealing with deep learning models that can take ages to train.
5. Explainable AI (XAI) As machine learning models get more complex, understanding why they make certain decisions becomes harder. That’s where explainable AI comes in. It’s all about making machine learning models more transparent and understandable, which is super important for trust and accountability.

And that’s a wrap on advanced topics! But remember, machine learning is an ever-evolving field with new concepts and technologies emerging all the time. So, keep an eye out, stay curious, and keep exploring. As you continue on this machine learning roadmap, I hope you’ll find these advanced topics as exciting as I do. The road ahead is full of possibilities. Can’t wait to see where you’ll go next!

Applications of Machine Learning

You’ve been such a trooper on our machine learning roadmap journey so far! We’ve explored the concepts, algorithms, and advanced topics, and now, we’re moving onto the fascinating world of machine learning applications. It’s time to see how all the pieces fit together in the real world.

1. Personalized Recommendations Ever wonder how Netflix seems to know exactly what you want to watch next, or how Amazon always suggests products you might like? That’s machine learning in action! Recommendation engines use algorithms to analyze your behavior and preferences, offering you a personalized shopping or browsing experience.
2. Autonomous Vehicles Self-driving cars are no longer a thing of science fiction, and we have machine learning to thank for that. These vehicles use machine learning algorithms to perceive their surroundings, make decisions, and navigate roads, all without human intervention.
3. Fraud Detection Machine learning is a powerful ally in the fight against fraud. Banks and financial institutions use machine learning to analyze transactions and spot patterns that might indicate fraudulent activity. It’s like a super-sleuth working round the clock to keep our finances safe!
4. Healthcare In the healthcare sector, machine learning is making waves. It’s being used to predict diseases, personalize treatment plans, and even assist in surgeries. It’s like having a super-smart, tireless assistant to help doctors provide the best care possible.
5. Natural Language Processing We talked about this in the advanced topics, but it’s worth mentioning again. Applications like virtual assistants, translation services, and sentiment analysis tools are all powered by machine learning. It’s like teaching a machine to understand and generate human language.
6. Image Recognition From tagging friends on social media to diagnosing diseases in medical images, machine learning algorithms can ‘see’ and understand images in impressive ways. They’re like high-tech eyes that never blink!

And there you have it, just a handful of the countless ways machine learning is being used today. I hope these examples illustrate just how integral machine learning has become to our everyday lives. But remember, this machine learning roadmap doesn’t end here. As technology advances and more data becomes available, new and exciting applications are bound to emerge. Who knows where we’ll go next? The road is wide open!

Steps to Master Machine Learning

Well spotted! Thanks for hanging in there with me. Now let’s shift gears on our machine learning roadmap and talk about the steps you can take to master machine learning. It’s a journey, but with patience and persistence, you’ll get there!

1. Start with the Basics

Well, as with any journey on our machine learning roadmap, you’ve got to start somewhere, and that’s with the basics. Trust me, there’s no need to be a superhero and leap tall buildings in a single bound right off the bat. So, let’s break down what these basics look like.

a. Mathematics

Let’s hit the first milestone on our machine learning roadmap: Mathematics. I know, I know, some of you might be thinking, “I signed up for machine learning, not a math class!” But, I promise, it’s not as daunting as it sounds, and it’s actually pretty essential.

1. Linear Algebra Let’s start with linear algebra. Why? Well, many machine learning algorithms rely heavily on it. You’re dealing with lots of data points, right? And these data points are often grouped together into vectors and matrices. Operations like dot products, matrix multiplication, and vector spaces are like the bread and butter of machine learning. So, having a good understanding of linear algebra is like having a secret weapon in your machine learning toolkit.
2. Probability Then we have probability. It’s all about understanding and quantifying uncertainty. If you think about it, machine learning is all about making predictions, and with predictions comes uncertainty. Probability helps us to measure and handle this uncertainty. It’s like your trusty flashlight on a dark and unknown path.
3. Statistics Last, but certainly not least, is statistics. Statistics is all about collecting, analyzing, interpreting, presenting, and organizing data. Sound familiar? That’s pretty much what machine learning is all about too! So, having a firm grasp on statistics is like having a detailed map to navigate your machine learning journey.

That’s the math part of our machine learning roadmap in a nutshell. Don’t worry if you’re not a math whizz right now. The best part is, you don’t have to be! With lots of online resources and tutorials available, you can learn at your own pace. Plus, the more you apply these concepts in machine learning, the more intuitive they’ll become.

b. Machine Learning Concepts

Our next stop on this machine learning roadmap brings us to the fascinating world of machine learning concepts. It’s like stepping into a new country and getting to grips with the local language. So, let’s explore some of these terms and concepts.

1. Supervised Learning Let’s kick things off with supervised learning. It’s like teaching a child how to ride a bike with training wheels. You have labeled data (that’s your training wheels) and the model learns from this data to make predictions or decisions without being explicitly programmed to perform the task.
2. Unsupervised Learning Then we have unsupervised learning. This is like letting the child explore on the bike without training wheels. You have unlabeled data and the model must find patterns and relationships in the data all on its own. It’s a bit more challenging, but also more exciting!
3. Reinforcement Learning Reinforcement learning is a bit different. It’s like teaching a dog to do tricks. The model learns to perform an action from many attempts, receiving rewards when it does well and penalties when it doesn’t. Over time, the model learns to make the best decisions to receive the most rewards.
4. Regression Regression is all about predicting a continuous value. It’s like trying to guess the temperature for tomorrow based on the weather patterns of the past few days.
5. Classification Classification, on the other hand, is about predicting a discrete value. It’s like a bouncer at a club, deciding whether you’re allowed in (you’re over 21) or not (you’re under 21).
6. Clustering Clustering is about grouping similar things together. Imagine you’re a librarian and you need to arrange books by genre. That’s what clustering algorithms do, but with data points instead of books.
7. Neural Networks Lastly, we have neural networks. These are the foundation of deep learning and are inspired by the human brain. They take in inputs, process them through hidden layers using weights that are adjusted during training, and finally, output a prediction.

Getting comfortable with these concepts is a key part of your machine learning roadmap. So take your time, learn them well, and you’ll be chatting in the language of machine learning in no time. Onward and upward, my friend!

c. Programming Fundamentals

Our journey on the machine learning roadmap takes us next to the land of programming fundamentals. Now, if you’re thinking, “I’m not a coder!” don’t panic! You don’t need to be the next Bill Gates or Grace Hopper to get a handle on this stuff. So, buckle up and let’s dive in.

1. Choosing Your Language First things first, you need to pick a language. Python is a popular choice in the machine learning world because of its simplicity and readability, making it a great starting point. Plus, Python has a ton of libraries that make the coding part of machine learning a breeze. It’s like having a Swiss Army Knife in your toolbox.
2. Understanding Syntax and Variables Once you’ve picked your language, you need to understand its syntax and how to use variables. Syntax is like the grammar rules of a language, and variables are like nouns, holding values that we can use and manipulate.
3. Mastering Data Types and Structures Next up, you’ll want to understand data types and structures. These are like the building blocks of your code. In Python, for example, you have numbers, lists, tuples, dictionaries, and sets, each with its own unique properties and uses.
4. Getting Comfortable with Loops and Conditionals Loops and conditionals are like the plot twists and turns in your code. They’re used to control the flow of your program, repeating actions and making decisions based on certain conditions.
5. Understanding Functions and Modules Functions and modules are like the supporting characters in your code, helping to break it up into manageable, reusable pieces. Understanding how to use them will make your code more organized, efficient, and much easier to debug.
6. Working with Libraries Finally, you’ll want to get to know the libraries available for machine learning. Libraries are like recipe books, providing pre-written code to do a lot of the heavy lifting for you. For Python, key libraries you’ll want to familiarize yourself with include NumPy for numerical computations, Pandas for data manipulation, and scikit-learn for machine learning.

Getting your head around programming fundamentals is a key step on your machine learning roadmap. It might seem a bit overwhelming at first, but remember, every coder starts somewhere. With practice, these concepts will become second nature, and you’ll be well on your way to mastering machine learning. So, let’s keep moving!

d. Data Structures and Algorithms

Alright, fellow explorer of the machine learning roadmap, we’ve arrived at a very crucial pit stop: data structures and algorithms. It’s like we’ve been hiking through the woods, and now we’re getting to the heart of the wilderness. I promise it’s not as scary as it sounds, so let’s break it down.

Data Structures Data structures are like the containers we use to store and organize our data efficiently. They’re super important because they determine how our data is stored, how we can access it, and what operations we can perform on it. In Python, you’ll come across things like:

• Arrays: Think of them as a line of people waiting in queue. Each person (or data item) is at a specific spot in the line.
• Linked Lists: Similar to arrays, but each data item knows what’s coming next in line, kind of like a scavenger hunt.
• Stacks: These are like a stack of pancakes. The last pancake you put on the stack is the first one you take off (we call this Last-In-First-Out, or LIFO).
• Queues: Now imagine a queue of people waiting to buy tickets. The first person in line is the first one to get served (that’s First-In-First-Out, or FIFO).

Algorithms Now, let’s talk about algorithms. These are the step-by-step instructions we give our computer to perform specific tasks. It’s like giving someone directions to find a hidden treasure. There are heaps of algorithms in machine learning, and it’s important to get familiar with the common ones:

• Sorting algorithms: Like Bubble Sort, Quick Sort, and Merge Sort. These are like different methods to arrange a deck of cards in a specific order.
• Searching algorithms: Like Binary Search and Linear Search. Think of them as different strategies to find a friend in a crowd.
• Tree-based algorithms: Like Decision Trees, Random Forests, and Gradient Boosting. They make decisions by following a set of yes/no questions.
• Graph algorithms: Like Breadth-First Search and Depth-First Search. These are different ways to navigate a maze.

Understanding data structures and algorithms is a crucial waypoint on our machine learning roadmap. They provide the tools and techniques we need to store, access, and manipulate our data efficiently. It might seem challenging at first, but with a bit of practice, it’ll all start to make sense.

Starting with the basics might not be the most glamorous part of your machine learning roadmap journey, but it’s essential. Just like you wouldn’t start decorating a cake before you’ve baked it, you shouldn’t dive into the deep end of machine learning before you’ve got a solid foundation.

2. Get Familiar with Programming Languages

Our next big step on the machine learning roadmap is about getting chummy with some programming languages. Yes, it’s about time we dived into the deep, sometimes confusing, but always exciting world of programming languages. Now, don’t sweat if you’re not familiar with any languages yet, because that’s the point! Let’s get started, shall we?

a. Python

Let’s start with the popular kid on the block: Python. This language is like the peanut butter to machine learning’s jelly. Python is widely used due to its simplicity, readability, and the vast number of libraries it offers for machine learning. From data manipulation with Pandas, to scientific computing with NumPy, to actual machine learning with scikit-learn, Python is a full-package deal!

b. R

Next up, we have R. R is to statistics what Python is to general programming: an extremely useful tool. It’s a language made by statisticians, for statisticians, so it’s got strong data visualization capabilities. If your machine learning roadmap is taking you down a path heavy in statistical analysis, R is a good friend to have.

c. Java

Then we have Java. It’s object-oriented, platform-independent, and has useful machine learning libraries like Weka and Mallet. While not as intuitive as Python for beginners, it’s heavily used in big data analytics and has a strong hold in the industry.

d. MATLAB

MATLAB, a proprietary programming language developed by MathWorks, might not be your first choice for machine learning, but it’s great for matrix manipulations, implementing algorithms, and data visualization. It’s widely used in academia and industries like robotics and signal processing.

e. Julia

Julia is a relatively new contender in the field but is gaining ground quickly. It’s designed to combine the speed of C with the usability of Python, the dynamism of Ruby, the mathematical prowess of MatLab, and the statistical chops of R. Quite a powerhouse, right?

In this part of our machine learning roadmap, it’s not about becoming a master in all these languages. Rather, it’s about getting familiar with them, understanding what each brings to the table, and figuring out which one aligns best with your machine learning goals. So, keep going, you’re doing great!

3. Learn the Tools of the Trade

Alright, we’re cruising down our machine learning roadmap, and we’ve come to an important pitstop: learning the tools of the trade. You see, in the world of machine learning, there are some pretty nifty tools that help make our work more efficient, and dare I say, even a little bit fun. So, let’s dive in and explore these bad boys.

a. Libraries and Frameworks

First up, we have libraries and frameworks. These are like your magic spells and potions that make the coding part a breeze. We’ve got:

• NumPy and Pandas: These are your bread and butter for data manipulation and analysis in Python.
• Matplotlib and Seaborn: These Python libraries are your artists, helping you create stunning data visualizations.
• Scikit-Learn: This is your one-stop-shop for a wide variety of machine learning algorithms.
• TensorFlow and PyTorch: These are your heavy lifters, powerful libraries for creating neural networks and diving into deep learning.

b. IDEs (Integrated Development Environments)

Next, we have Integrated Development Environments or IDEs. These are like your workbenches where you write, test, and debug your code. There’s a variety to choose from:

• Jupyter Notebook: Great for data cleaning, statistical modeling, data visualization, and machine learning. It allows you to create and share documents that contain live code, equations, visualizations, and narrative text.
• Spyder: A Python-specific IDE, perfect for scientific computing because of its integration with libraries like Matplotlib, SciPy, and NumPy.
• PyCharm: A powerful IDE for Python from JetBrains, offering smart code assistance, easy navigation, and rapid error fixing.

c. Cloud Platforms

Last, but certainly not least, we have cloud platforms. These are your powerhouses, providing the computing muscle you need to handle large datasets and run complex machine learning models.

• Google Cloud ML Engine: This allows you to run your machine learning models on the cloud, using Google’s powerful infrastructure.
• AWS SageMaker: Amazon’s cloud platform provides a complete set of tools for machine learning, from building, training, to deploying models.
• Microsoft Azure ML Studio: This cloud-based, drag-and-drop tool lets you build, test, and deploy predictive analytics solutions on your data.

Getting comfortable with these tools is a crucial step on your machine learning roadmap. But remember, it’s not about learning all the tools at once, it’s about understanding what each one offers and finding out which ones work best for you.

4. Get Hands-On with Projects

Okay, my machine learning enthusiasts, we’re revving up our engines and zooming ahead on our machine learning roadmap. Now, it’s time to roll up our sleeves and get our hands dirty with some real-world projects. Why, you ask? Well, it’s one thing to learn about machine learning in theory, but applying that knowledge to practical problems is where the real learning begins. So, let’s gear up and get started!

a. Beginner Projects

If you’re just starting, pick simpler projects. They’re like the warm-up before the marathon, helping you understand the basics of implementing machine learning models. Here are a few examples:

• Iris Flower Classification: It’s a classic dataset where you’ll classify iris flowers into one of three species based on the sizes of their petal and sepal.
• Digit Recognition: Using the MNIST dataset, you’ll be building a model to recognize handwritten digits. Think of it as creating a mini version of the postal service’s handwriting recognition system!

b. Intermediate Projects

Once you’ve got the basics down, you can move onto slightly more complex projects. It’s like going from riding a bike with training wheels to riding a real mountain bike. Here are a couple of ideas:

• Movie Recommendation System: Using a movie dataset, you’ll build a system that recommends movies based on a user’s viewing history.
• Email Spam Detection: You’ll be creating a model that filters out spam emails from the real ones, saving people from the hassle of junk mail.

c. Advanced Projects

When you’re ready, dive into more advanced projects. They’re the marathon itself, challenging you to use all the knowledge and skills you’ve acquired. Consider:

• Stock Price Prediction: Using historical stock prices, you’ll predict future stock values. But remember, no insider trading!
• Chatbot Creation: Here, you’ll be creating a chatbot that can understand and respond to human language. It’s like having your very own personal assistant.

Embarking on these projects is a thrilling part of your machine learning roadmap. They offer you invaluable practical experience and make your learning journey all the more fun. Plus, they give you a pretty impressive portfolio to showcase your skills. So, what are you waiting for? Dive in and start exploring!

5. Understand the Machine Learning Process

Alright, my machine learning mavens, we’re steering our way further down the machine learning roadmap. Next up, we’re going to understand the machine learning process. You see, building a machine learning model isn’t just about knowing the right codes or algorithms. It’s about understanding the whole process from start to finish, like knowing how to make a delicious meal from scratch. So, let’s put on our chef’s hat and dive in!

a. Defining the Problem

First things first, we need to define the problem. What is it that we’re trying to solve with machine learning? It’s like deciding what dish we’re going to make. Is it a main course, a dessert, or maybe a soup? For machine learning, are we trying to predict something (a regression problem), categorize something (a classification problem), or discover patterns (a clustering problem)?

b. Gathering the Data

Next, we need to gather our ingredients, or in machine learning terms, our data. This could come from various sources like databases, data files, web scraping, APIs, etc. It’s all about finding the right ingredients that will help us solve our problem.

c. Data Preprocessing

Now that we have our ingredients, we need to prepare them. This is one of the most critical parts of the machine learning process. It involves cleaning the data, dealing with missing values, outliers, and unnecessary information. We may also need to normalize or standardize our data, or maybe even perform some feature engineering.

d. Building the Model

This is the part where we actually start cooking. We pick the right machine learning algorithm for our problem and train it with our preprocessed data. It’s like combining our ingredients in the right order and cooking them at the right temperature.

e. Evaluating the Model

Once our dish (or model) is ready, we need to taste it. We evaluate our model based on relevant metrics. It could be accuracy, precision, recall, F1 score, or something else depending on our problem. This gives us an idea of how well our model is performing.

f. Improving the Model

If our dish doesn’t taste right, we tweak the recipe. Similarly, if our model isn’t performing well, we can tune the algorithm, use different features, or even try a different machine learning algorithm.

g. Deploying the Model

The final step is to serve the dish. Once we’re satisfied with our model’s performance, we deploy it so that it can be used to make predictions on new, unseen data.

Understanding this process is a key milestone on your machine learning roadmap. Remember, machine learning isn’t just about reaching the destination, it’s about enjoying the journey. So, take your time, understand each step, and before you know it, you’ll be whipping up fantastic machine learning models like a pro!

6. Dive into Deep Learning

Well, folks, we’ve now arrived at a thrilling stop on our machine learning roadmap: deep learning. Think of deep learning as a hidden treasure in the ocean of machine learning. It’s a subset of machine learning that’s particularly good at dealing with large amounts of data, and it’s behind some of the most cutting-edge technologies today, like self-driving cars and voice assistants. So, buckle up, it’s time for a deep dive!

a. Understanding Neural Networks

Deep learning primarily works with artificial neural networks. Picture these as an attempt to replicate our brain’s workings in a machine. Cool, right? These networks consist of layers of nodes, or ‘neurons’, which process information and pass it on. The more layers, the ‘deeper’ the learning, hence the term ‘deep learning’.

b. Types of Neural Networks

Not all neural networks are the same, and different types are good for different tasks. Just like how different cars are better for different roads:

• Feedforward Neural Networks: These are the simplest type of neural network, where information moves in one direction: forward from the input nodes, through the hidden layers, to the output nodes.
• Convolutional Neural Networks (CNNs): These are great for image processing tasks like image recognition. They’re like your expert photographers.
• Recurrent Neural Networks (RNNs): These are ideal for sequential data, like time series or sentences. They’re like your well-trained stenographers.
• Generative Adversarial Networks (GANs): These consist of two neural networks contesting with each other in a game. They’re like your competitive game players.

c. Deep Learning Libraries

We have some very handy tools to help us navigate the deep learning seas. Libraries like TensorFlow and PyTorch provide the frameworks needed to build and train these complex neural networks. They’re like your scuba diving gear for the deep learning ocean.

d. Applications of Deep Learning

Deep learning is behind some of the coolest tech out there. From self-driving cars that can recognize pedestrians and other vehicles, to voice assistants that can understand and respond to your commands, to systems that can generate art or even music, deep learning is shaking things up across industries.

Taking a dive into deep learning is an exhilarating part of your machine learning roadmap. It’s a challenging but rewarding journey that opens up a world of possibilities. So take a deep breath, dive in, and explore the wonders of deep learning!

7. Learn Deployment

And now, my machine learning adventurers, we’ve arrived at an incredibly important destination on our machine learning roadmap: deployment. You see, creating a machine learning model is kind of like building a fancy sports car. It’s cool and all, but what good is it if it just sits in the garage? To truly make an impact, our models need to get out on the road, or in other words, be deployed. So, let’s learn how to take them for a spin!

a. Understanding Deployment

Deployment in machine learning is the process of integrating a machine learning model into an existing production environment so that it can take in input data and return output. It’s the bridge that connects your cool machine learning project to real-world, practical use.

b. Deployment Options

Like how you might choose between highways, city streets, or scenic routes for driving your car, there are different ways to deploy your machine learning model:

• Cloud-Based Platforms: Services like AWS SageMaker, Google Cloud ML Engine, and Microsoft Azure ML Studio allow you to host your model on the cloud, making it accessible from anywhere.
• On-Premises Deployment: In this case, the model is hosted on local servers. It’s like driving your car on your own private track.
• Edge Deployment: Here, the model is deployed on IoT devices and can make predictions even without network connectivity. It’s the equivalent of an off-roading adventure!

c. Considerations for Deployment

Deployment isn’t just about choosing a route. You also need to consider factors like:

• Scalability: Can your model handle increasing amounts of data or requests?
• Latency: How fast can your model process input and return output?
• Security: How are you protecting the data involved?
• Monitoring: How will you monitor the model’s performance and make updates if needed?

d. Containerization and Microservices

Tools like Docker and Kubernetes come in handy for deployment. They allow you to bundle your model and its dependencies into a ‘container’, making it easier to deploy and scale. It’s kind of like putting your car on a trailer to transport it to different racetracks.

Learning to deploy your models is a critical step on your machine learning roadmap. It’s where the fruits of all your hard work become visible to others. So, gear up, put the pedal to the metal, and get those models out there for the world to see!

Resources For Learning ML 

Machine Learning Resources

These are the resources you can use to become a machine learning or deep learning engineer. All of the resources are available for free online. Please check their respective licenses.

Machine Learning Theory

• Machine Learning, Stanford University
• Machine Learning, Carnegie Mellon University
• Machine Learning, MIT
• Machine Learning, California Institute of Technology
• Machine Learning, Oxford University
• Machine Learning, Data School

Deep Learning Theory

• Deep Learning, Ian Goodfellow
• Neural Networks and Deep Learning
• Understanding LSTM Networks
• Deep Residual Learning

Forward and Backpropagation Theory and Code

• Step by Step Forwardpropagation and Backpropagation with Numbers
• Full Manual Backward Propagation with TensorFlow
• Reverse Mode Automatic Differentiation with TensorFlow
• Simple Backward Propagation with Python
• Backward Propagation from Scratch with Python
• Neural Networks Demystified with Python, Welch Labs
  • Part 1: Data and Architecture
  • Part 2: Forward Propagation
  • Part 3: Gradient Descent
  • Part 4: Backward Propagation
  • Part 5: Numerical Gradient Checking
  • Part 6: BFGS Numerical seOptimization Algorithm
  • Part 7: Overfitting, Testing and Regularization

General Machine Learning with Python and Scikit-learn

• Machine Learning with scikit-learn, Data School
  • Machine Learning with scikit-learn
  • Comparing Supervised Learning Algorithm
  • Machine Learning with text
• Machine Learning with scikit-learn, Jake Vanderplas
  • Introduction to scikit-learn
  • Basic Principles
  • Linear Regression
  • Support Vector Machines
  • Regression Forests
  • Dimensionality Reduction
  • k-means Clustering
  • Density Estimation
  • Validation and Model Selection
• Decision Trees, The Grimm Scientist
• Machine Learning with scikit-learn, Andreas Mueller
  • Introduction to Machine Learning with Python
  • Scikit-learn tutorial
  • Advanced scikit-learn
• Convolutional Neural Networks with Python, Stanford
  • CS231n Convolutional Neural Networks for Visual Recognition

Convolutional Neural Networks with TensorFlow/Keras

• Deep Learning Models like VGG, Inception V3, ResNet and more in Keras
• Practical Deep Learning with Keras, Jason Brownlee
• Wide Residual Networks in Keras
• Wide ResNet in TensorLayer
• TensorLayer Official Tutorials

Reinforcement Learning Theory

• Reinforcement Learning Introduction, Nervana
• Reinforcement Learning, Sutton
  • Programming Exercises for Sutton’s Book
• Uncertainty Estimates from Dropouts

Reinforcement Learning with TensorFlow/Keras

• Using Keras with DPPG to play TORCS
• Advantage async actor-critic Algorithms (A3C) and Progressive Neural Network in TensorFlow

Recurrent Neural Networks Theory

• Understanding LSTMs

Recurrent Neural Networks with TensorFlow

• RNN Official TensorFlow Tutorials
• RNN-LSTM with TensorFlow
• Introduction to RNN in TensorFlow
• Advanced RNN guides and code
• RNN in TensorFlow with and without API
• RNNs in TensorFlow, A Practical Guide and Undocumented Features
• TensorFlow code for Latest RNN Papers

Mathematics Useful for Machine Learning

• Discrete Mathematics, MIT
• Linear Algebra, MIT
• Linear Algebra Review, Stanford
• Probability Review, Stanford
• Convex optimization overview, Stanford
• More convex optimization overview, Stanford
• Single Variable Calculus, MIT
• Practical Guide for Matrix Calculus for Deep Learning

Deep Learning Environment

• One-click AWS AMI with TensorFlow and Keras

Best Books

Personally, I’ve found books to be the best source of knowledge after going through the courses. This is where you can strengthen your theoretical understanding of the concepts that you use in your ML projects.

1 – The Hundred-Page Machine Learning Book by Andriy Burkov
A very short book but with perfect knowledge. Andriy has compressed all the vital points in AI/ ML and put it in this 100 pages book[138 to be precise].

2 – Hands-on Machine Learning with Scikit-Learn, Keras and Tensorflow 2.0 Book by Aurelien Geron — O’Reilly
According to me, this book is an alternative to the Machine Learning and Deep Learning specializations by deeplearning.ai. I prefer this book as it has perfect explanations and every concept has a good code to try out side by side. You can also access the open-sourced code from this book at the following link — https://github.com/ageron/handson-ml2

3 – Deep Learning book by Ian Goodfellow
If you want to get deeper into the mathematical side of deep learning then this book has everything that you need. It was published in 2015, so it is relatively old but the content is great.

Bonus Book

Life 3.0 by Max Tegmark
Life 3.0 isn’t for learning AI and ML but it is a beautiful book that discusses the impact of Artificial Intelligence on the future of the human race and cosmic influence. The views of the author are interesting and it is indeed a great read.

Theory of Machine Learning

As a machine learning engineer, you should be a master of the following concept:

• Clean Data
• Fill Missing Value
• Drop Some Feature
• Feature Selection
• Feature Scaling
• Regularization
• Feature Engineering (optional at first)
• Regression Algorithms
• Simple Linear Regression
• Ridge & Lasso
• Multiple Linear Regression
• Polynomial Regression
• XGBRegressor
• Classification Algorithms
• KNN (K Nearest Neighbor)
• Logistic Regression
• Decision Tree
• Random Forest
• Naive Bayes
• XGBClassifier
• Clustering Algorithms
• K-Means
• DBSCAN (Density-based spatial clustering of applications)
• Dimensionality Reduction
• PCA (Principale Component Analysis)
• LDA
• t-SNE

Machine Learning in Practice

You should learn sklearn to implement all last step concepts. There are also many courses for this library. You can also use sklearn documentation.

Frequently Asked Question

• What are different types of Machine Learning algorithms?

Machine Learning algorithms are typically broken down in the following categories

• Supervised learning – They are set of algorithms which predict an output given an input based on example input output data given to the algorithm.
• Unsupervised learning – They are set of algorithms which try and find undiscovered patterns in a dataset without any example input output data given to the algorithm.
• Reinforcement learning – Reinforcement learning is trial and error learning, where the program tries different strategies and learns from its mistakes and success to get better at the task.
• How Does Machine Learning Work?

Using a multitude of analytical programmes, algorithms are developed and refined within a process in accordance with your business questions. Machine learning looks at the history of your current data and detects patterns within it, and then adjusts its future actions accordingly. Its main aim is to both clean your data, and make predictions towards future data sets. 
Machine learning statistical methods such as clustering, regression and classification are used in predictive analytics.

• What Can Machine Learning Do For Me And My Business?

Machine learning in its simplest form will automate repetitive tasks. Data collection, sorting, entry and transformation can all be automated, saving your business crucial time and resources. In a more refined form, it’s able to tell you where and how your business is being successful, and make predictions regarding your businesses’ future.

• What Do I Need To Start?

To be able to apply machine learning you need a problem to solve, and you need data relating to this problem. Your data can preferably be in a structured form (within a database or multiple spreadsheets), or an unstructured form (emails and social data).

• Where Can I Find Some Use Cases?

Product recommendation engines, such as those used by Amazon and Netflix, are produced with machine learning at the core of their design. These have been proven to increase revenue and interaction drastically, with Amazon stating 35% of their total revenue comes from their product recommendation engine.

• Is Machine Learning Expensive?

This completely comes down to the exact process that you’re after, and whether or not you go with a bespoke or off the shelf system. Whilst off the shelf systems typically work on a pay as you go basis, their algorithms are often limited in flexibility. Bespoke packages on the other hand typically test and refine the models. Pilots start at a few thousand pounds and can rise with increased development.

• What is a regression problem and what is a classification problem? Give examples?

Regression problem is a problem when output variable is continuous, a classification problem is a problem where output variable is discrete. Example of a regression problem is predicting temperature of New York City next day, example of a classification problem is predicting if a Tumor is malignant or benign.

• What is cross validation?

Cross validation is a modeling technique for assessing how the statistical model will generalize to out of sample data. Cross validation is also used to search or select model hyperparameters.

There are various Cross Validation techniques:

1. Holdout cross validation.
2. k-fold cross validation.
3. nested cross validation.

Conclusion

Well, my machine learning friends, we’ve come to the end of our exciting journey down the machine learning roadmap. What a ride it’s been! From understanding the basic concepts, getting familiar with the process, algorithms, diving deep into the realm of deep learning, and finally learning how to deploy your models, we’ve covered quite a bit of ground.

Remember, machine learning isn’t just about memorizing algorithms or getting the highest accuracy on your model. It’s a field that’s about curiosity, exploration, creativity, and continuous learning. It’s about solving real-world problems and making a positive impact on the world.

As you continue your machine learning journey, don’t be afraid to ask questions, try new things, and make mistakes. Every detour, every roadblock, and every success is a part of your unique journey. And trust me, every step you take along your machine learning roadmap brings you closer to becoming the machine learning expert you’re destined to be.

So, keep learning, keep exploring, and most importantly, have fun while you’re at it. After all, the joy is in the journey as much as it is in the destination. Happy learning and all the best on your machine learning adventure!