How To Learn Artificial Intelligence Complete Guide

How To Learn Artificial Intelligence In 2021 [Complete Guide]

If you want to get into artificial intelligence, the first thing you must have asked yourself is, what is artificial intelligence really? Then from there, the hunt begins.

Everyone has their own definition of what artificial intelligence is. And there seems to be no consensus as to what this term actually refers to. Well, not anymore.

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In this article, we are not only going to define exactly what artificial intelligence is, but we are also going to look at a step by step process to follow if you want to get into artificial intelligence.

These are the exact steps you need to take if you want to learn artificial intelligence in 2021.

But before we go any further, let’s first clear the air on what artificial intelligence is.

What is artificial intelligence?

Artificial intelligence (AI) is a branch of computer science that is concerned with building machines that are smart enough to perform tasks that typically would require human intelligence and intervention.

It is a vast discipline that integrates different approaches and models, but the most conspicuous advancements in artificial intelligence have been seen in machine learning and deep learning.

But even with that definition, understanding what AI really is can still be challenging as these terms can be used interchangeably.

For example, you’ll meet questions like: what’s the difference between artificial intelligence and machine learning? What about machine learning and deep learning? And speech recognition and natural language processing?


Now you begin to lose your mind.

Well simply put, think of artificial intelligence like any kind of human-like intelligence exhibited by a machine, a bot or a computer.

So with artificial intelligence, a machine can learn from examples, past experiences, recognize objects, understand language, make decisions and solve problems.

Clearer now?

Good. Now that we’ve answered the question: what is artificial intelligence, it’s time to now get right into the details of how to get started learning artificial intelligence in 2021.

But before we get into the, how to learn artificial intelligence guide, how about we look at a few AI applications to give you an idea of what we are talking about here?

Applications of artificial intelligence

Where is artificial intelligence actually used?

While there are many different applications of artificial intelligence in 2021, for the purpose of brevity, we are only going to look at the 3 most common uses of AI.

However, I’ll later write another article detailing all the uses of AI today.

1. Marketing

Have you ever asked yourself how your Netflix addiction came about?

How video streaming platforms like Netflix and YouTube use artificial intelligence to keep you hooked is the first thing that came to mind when I thought of AI uses in marketing.

It’s like these platforms read our minds with close to human-like precision.

Netflix will closely watch your interactions with other movies, series and films on their platform and then use their predictive technology to provide you with a highly accurate suggestion of films and shows that you might like, based on your previous behaviour.

2. Banking

Have you ever suspected chatting with a bot instead of a human on a support website?

Well, a lot of companies, including banks, have adopted artificial intelligence technologies to provide customer support.  

But that aside, the most interesting application of AI in baking is when it comes to detecting anomalies and fraud. While the use of AI for fraud detection might not be a new concept, its use has really enhanced security across various sectors including banking.

Through AI banks have been able to trace endpoint access and card usage to effectively prevent fraud.

3. Trading

Lastly, let’s look at how the pros are using artificial intelligence to make a killing in the stock market

Ever heard that saying that you can beat the house in gambling?

Well, your success in stock trading squarely depends on your ability to predict the future accurately. Seems like gambling to me. Except you don’t go with your gut, you use data 🙂

Data scientists have developed stock trading machines that can learn to observe patterns in the past and use that to predict how these patterns might repeat in the future, thereby enabling financial organizations to improve their stock trading performance and boost profits.


Artificial intelligence is everywhere these days.

This has led to a super high demand for artificial intelligence engineers. And so it’s understandable that you want to learn how to get started learning artificial intelligence from scratch in 2021.

So let’s get right into it.

How to learn artificial intelligence

How exactly do you learn artificial intelligence and launch a successful career in this industry?

Now there are two categories of people or interests here.

There are those who want to get into artificial intelligence for the purpose of academics and research, and then there those whose focus is applications, so that they can finally land a well-paying job in AI.

I am not a research guy, I don’t have a PhD, leave alone a masters.

So if you are looking for a guide to lead you into AI for academic research, I’ll give you a minute to leave the room. This guide is only targeting software engineers who want to work in AI.

Okay, good.

So in this section, I am going to show you how to learn artificial intelligence from scratch, the skills you need to master, how to get the word out there of your new-found genius skills, and landing your first job as an AI engineer.


Let’s get started.

1. Learn Math

I know I said that this guide is not for those wanting to get into AI for the purpose of research… I didn’t forget that.

So you may be like: but Math is for researchers, right?

Not exactly. Unfortunately, there is no going around learning Math if you want to get started learning artificial intelligence in 2021.

From my experience developing artificial intelligence applications, a proper foundation in linear algebra and calculus is so crucial that if I heard someone telling beginners in AI to skip this part, I wanna smack them in the face.

Here is why. For you to work with machine learning algorithms you need to be good in algebra. Besides, training neural networks takes a good dose of calculus skills.

When getting started with artificial intelligence there is this excitement to start coding something.

But that also means that you are going to wing your way through it, with a ton of trial and error. This leads to a lot of headache and frustration. In addition, the last thing your boss wants to discover, after being hired, is that you actually don’t know what you are doing.

So for a start, I suggest that you first spend some quality time with a proper algebra and calculus course.

Ensure you practice along with the practice examples, instead of just nodding your head along. If you don’t  then all you get to learn is nothing.

Now, I am not saying that you must master algebra and calculus inside out before you can get started with artificial intelligence from scratch. But you do need at least a proper understanding of the basics to get started.

In addition to Math, I’d also suggest brushing your memory a little bit with some probability and statistics tutorial.

2. Learn Code

Thinking of a black screen with some weird characters that look like English?

After learning Math, the next logical step in getting started with artificial intelligence is learning to code is some kind of programming language.

AI applications are basically software that is programmed and made to run on the target machines, giving them additional human-like intelligence to make observations, learn and make decisions.

And this has to be done in some kind of a programming language.

Now I don’t want to blindly push hard for Python, which is my favourite programming language for AI here… because there are a ton of other AI programming languages that just do as good a job.

My advice here is to rather go with a mainstream programming language instead of a lesser popular language for two reasons:

  • a popular programming language will provide you with a ton of ready-made artificial intelligence tools and libraries that are high quality and work out of the box.
  • a mainstream language might also have the advantage of standing out in terms of performance as their developers have had time to tweak them enough.

Having said that, some of the more popular programming languages for artificial intelligence include C++, Java, Python and even R.

The point here is to choose just a programming language and get started.

DON’T try to master the language inside out, or try to memorize all the commands and functions in the language, as this is completely unnecessary when it comes to learning artificial intelligence as a beginner.

What you should get good at, instead, is how to search for solutions to certain coding problems on the internet.

If you choose to go with Python for example, I would opt for a Python tutorial that is geared towards beginners in artificial intelligence instead of a general Python course that does not target anything in particular.


Because while it would be great to have all the Python basics and then go to a more focused Python course, it would also mean that you have to put a lot of time upfront. However, starting with a Python for AI tutorial will introduce you to the commands and functions specific to AI early on.

Remember that learning artificial intelligence is a venture most people start but never see through to the end… they get overwhelmed and give up.

3. Choose a Focus

If you are still following along then big ups to you, we are headed down a path that is for sure going to lead you into a promising and amazing career.

I know you’ve now got the Math and statistics skills nailed down. 

You’ve also learned a programming language and can’t wait to get started building some AI applications and wow your followers on Twitter.

But, you have to back up a little bit.

When we started this article, I mentioned that AI is such a vast discipline, traversing various other interdisciplinary sciences. You can’t go at with a catch all mentality.

So the next step in learning artificial intelligence from scratch in 2021 is choosing which branch of AI you want to get into… if you have a particular problem you want to solve, the better.

Most online guides on how to get started with artificial intelligence won’t tell you this, but the main reason for burnout and despair among AI beginners is going at it without some particular focus or goal.

While talking about these types of AI will make it easier for you to choose which direction you wanna head, it also opens another pack of worms as the online community is yet to agree on how many types of AI there actually are.

Besides, knowing the types of AI will not even help you decide on what you particularly want to learn.

Types of AI

Here are the two most mentioned types of of AI:

  • Weak AI, also known as Narrow AI, refers to the types of AI applications that focus on performing one task at a time while continuing to improve its execution. Examples are self-driving cars, face recognition software, Google Translate, Apple’s Siri etc.
  • Strong AI, also known as General AI, refers to a more comprehensive machine intelligence that, as opposed to focusing on one single task, teaches the machine to comprehend and reason on a wide level, just like a human. Currently, there is no real implementation of Strong AI.

You see?

This categorization is completely useless in helping you decide which direction to take when getting started in AI as a beginner, because almost anything you’d like to build, or you could possibly build, is in category one.

That’s why I mentioned that it would be easier to find your focus if you had a particular solution or project in mind.

But if you still have no clue here, then I have something that might help you get a clearer idea of what you want to do in artificial intelligence.

Let’s look at the most common technologies in AI. Since these technologies have different capabilities, it can at least give you an idea of what to focus your attention on learning. The better you get at any one of these technologies the easier it will be to branch into the others.

AI Technologies

These are the technologies that you’ll interact with mostly as an AI developer. Most of them derive from AI or from each other.

However, note that this is not an exhaustive list. There are a lot of AI technologies and disciplines with their own branches of mathematical and engineering study.

But these are the most relevant ones that I recommend a beginner getting into AI to look into.

  • Machine Learning. Often you’ll see AI mentioned along with ML. ML is the branch of AI that aims at building machines that can learn from data, identify patterns and make decisions without human intervention. It’s at the heart of most commercial AI apps.
  • Deep Learning. It is a branch of machine learning that focuses on training a computer to perform talks like recognizing speech, identifying images and making predictions by implementing algorithms that use artificial neural networks.
  • Natural Language Processing. It is a branch of artificial intelligence that teaches computers to understand, interpret and manipulate human language by using text analytics to analyze the sentence structures, interpretations and intentions.
  • Computer Vision. In this branch of artificial intelligence where you train computers to interpret and understand the visual world. Great examples of applications of this involve face recognition, image search and licence plate recognition.
  • Robotics. Think of robotics like taking functioning corporate processes that mimic human behavior and automating them. This is where AI sends chills down the spines of most people as they think AI will replace humans. Yes, partly, but not entirely.


This part on choosing your focus was very lengthy, and I barely covered it.

So if you are a complete beginner who wants to learn AI from scratch I hope you now get the idea of what you could focus on at this point.

To wrap up this step on choosing a focus, I would say that the best way to do it is to have a particular well defined goal in mind.

For example, you could say 

“I want to define an algorithm that can predict the weather”.

From there it is very easy to organize and focus your energy around this particular goal so that you are not overwhelmed by the immense amount of possibilities with AI today.

If you’ve not yet nailed your objective for learning AI, take a minute and get this straight first. It will really help moving forward.

Okay, now let’s move to the next step which has to do with finding the right learning materials.

4. Take a Course

In this day and age, there are a ton of learning materials that are available for free that could help you hone any particular skill in artificial intelligence.

But this amount of choice also comes with overwhelm… as you now don’t know which material is the best for you and are scared of wasting your time.

Either way, I think a great video tutorial is a great way to go.

And if you already decided on what particular thing you want to build from the previous step, it is easy for you to narrow down your search for learning materials to the kind that targets the technology you want to learn.

When I started learning AI, I used a couple of books.

Later I discovered video tutorials online, especially on YouTube and I have never looked back. In fact, from my experience it is better to work with a video tutorial than a book when learning AI as a beginner.

It is easier to follow along with a video tutorial.

I don’t want to spend much time here because when it comes to video tutorials each one has their own preferences. However, here is a simple 3 step process that I use to find tutorials for any subject I am trying to learn.

  1. Type in Google or YouTube the technology you are trying to learn together with the keyword “tutorial”.
  2. Try every tutorial from the results that show up until you find the instructor whose voice, organization and teaching approach you like.
  3. Take the course and follow it religiously through to the end. Don’t switch tutorials in between learning or you’ll suffer from information overload.

While doing this, your main objective is to focus on absorbing or soaking up as much theory and knowledge as possible on artificial intelligence.

If you find a great tutorial, it will also have some practice examples and tests that you should take along with the instructor, to ensure that you actually understood the concepts.

Again, the key here is to actually practice and not just nod along.

Now, you’ll find two kinds of tutorials.

First are those that delve deep into artificial intelligence theory and best practices without any practical aspect. That is to say, you will not be writing any code. These are the courses you want to start with.

Second are the ones that are jam packed with practical coding practice and exercises. These are the ones where you actually implement algorithms, feed them data and see the output and optimize. These are the ones you follow with. They help you to cement the theory you picked up earlier.

While you’ll majorly be following along with the instructor, I still find these tutorials helpful and fun at the same time as you’ll be learning a ton.

Once you are good with this hand holding, you want to move to the next step in learning artificial intelligence from scratch in 2021.

5. Build a Project

In the previous section, you learned how to find the best tutorials that will actually help you learn artificial intelligence theory and practice.

But the most fun but difficult part is now building your own models and applications without being hand held by a tutorial instructor. 

In this section of getting started with artificial intelligence, let’s look at how to go about building your own projects, developing a portfolio and finally deploying your AI models.

Also remember that when you will be looking for a real AI job, later at step 7, you’ll really need these portfolio projects you’ve been building. They’ll give you an opportunity to demonstrate what you are capable of doing during an interview. In this field, you don’t get a job without experience.

Here are some three tips to help navigate the murky waters of building an AI application.

  • Start by solving a simple and easy problem. And while at it, you want to experiment with different approaches to harness the power of algorithmic decision making.
  • Next, optimize your basic solution through experimentation. So you  basically upgrade various components while you monitor the results to find which has the quickest solution.
  • Remember to make all this gradual. Like, start by writing simple neural networks, then gradually complicate them as you get comfortable.

I am currently working on an article of the best artificial intelligence projects for complete beginners to get you started with. Once the article is ready I’ll come back and update the link.

In the meantime here are 3 artificial intelligence project ideas that I find interesting and quite challenging at the same time for beginners:

  1. Housing price prediction. I’d pick a random city, New York, and then try to predict the selling price of a new home there. For this I’ll need a dataset consisting of the prices of different homes in different areas of the city.
  2. Stock price prediction. Beginners in artificial intelligence and machine learning find share market prediction very interesting. It’s because there is already a ton of data out there that you can get started with.
  3. Customer recommendation. You want to think of a customer product recommendation system for an ecommerce platform like Amazon. In this case your data source is mainly going to be the customer browsing or purchase history.

Now, while it may all sound rainbows and sunshine, the main problem you are quickly going to face when you start building your own AI projects is that, you need a ton of data for any of these projects to yield meaningful results.

And this data is often hard to come by.

So here are 3 main data sources that have been very useful for me when I need datasets to work with. 

And the good thing? The data is FREE.

  1. OpenML
  2. Google Research
  3. ImageNET

With all this, I think you now get the idea of what you  need to do when it comes to building your personal projects.

Of course you are also going to need some tools to help you do your job.

So I wrote another article about the best data science tools that I think might form a useful addition to your arsenal of AI tools.

But to give you a hint, here are the 3 top tools you are going to be interacting with:

  1. TensorFlow
  2. SciKit
  3. Deeplearning4j

Let’s now wrap this up and move onto the next step in the guide on getting started with AI from scratch. It’s getting too long.

We’ll discuss how to deploy your applications in the next section.

6. Deploy & Compete

If you’ve come this far I am sure that you now probably have a very amazing AI application that you are ready to share with the world.

And so you think you are just going to deploy it on shared hosting like a WordPress blog.

Unfortunately, successfully implementing an artificial intelligence or machine learning application demands some significant hardware requirements. And most shared hosting providers will not work out for you.

So what are your options?

  1. Build a powerful computing hardware
  2. Use a cloud base supermachine

Deploying your models is the part of learning artificial intelligence that most learns skimp on because of the complexity and costs involved.

But being able to demonstrate these model deployment skills in your resume will put you ahead of everyone else.

I coupled deploying with competing because once your models are accessible in the cloud, it is possible to use them as a portfolio or for demonstrations…

But most importantly, is because at this point, you  need to put your skills to test.

How do you match up with other AI engineers?

All along you’ve been working on your own problems. Problems that you identified and solved. 

Now this might have some bias to it for a number of reasons:

  1. You might have chosen easy problems that did not challenge you enough. 
  2. In the real world you don’t get to choose the kind of AI implementation you want a company to build. They decide what they want then they bring you on board.
  3. If you are going to work for a company, you most probably are going to join a team. But AI theory does not teach team collaboration.

Onc advantage of joining a competition is because you get to put your newly acquired skills to practice.

You do this by getting to work on problems that other developers are working on. And the best way to get started with this is to join a Kaggle competition.

So apart from the amazing opportunity to put various approaches to test to find the most appropriate solutions for the competition project challenges, you get to learn very important team collaboration skills.

Through these competitions you’ll get to network and meet other people that might be key in helping you advance in your career.

In the process of working on a project, you get to learn how to ask questions in AI, where to ask these questions, how to share your ideas and how to keep yourself updated with the latest developments in AI.

Because, I can tell you for free that this field changes so rapidly that a 1 year skill gap might look like a whole decade.

You might also win money from a competition.

We’ve now almost moved through the whole process of learning AI from scratch as a complete beginner to deploying your first application and participating in a competition.

One last bit that is left is to now go get some real job.

7. Work

Shouldn’t you now be ready for a job?

If you’ve worked in the software development industry for a while then you definitely are aware of the imposter syndrome that developers suffer from in the early states of their careers.

It’s where you’ve learned to code, have built apps, but since you’ve never really worked in a corporate environment or on a real customer facing application, somehow you feel inadequate. Like your skills are still really wanting.

And the only way to get rid of it is to get a real job and work, and then work some more.

So at this point what I’d tell you is to get a job.

Now, this is where most AI developers hit a wall and give up.

Despite the fact that AI engineers are in very high demand, getting a job in this sector is still competitive and requires a strategy.

Here is a quick 5 step strategy to help you land your first AI job:

  1. Post your projects on GitHub. Hiring managers want to see your code and your actual implementations. So ensure that all your projects are publicly available on GitHub, possibly with links to where you deployed them.
  2. Create a LinkedIn profile. LinkedIn is a professional networking platform that most hiring managers hit first, when looking to hire in AI. Spice up your profile on LinkedIn, making sure you mention and link to your GitHub projects from there.
  3. Blog about artificial intelligence. After learning all that coding, problem solving and Math, you definitely have got something to say about AI and ML. Post something related to these on Medium. A reader there might be your future boss.
  4. Participate in meetups. Apart from participating in competitions, another way to network and put word out there about your skills is to join AI related meetups. By being active in these groups, like doing presentations, you’ll finally get noticed.
  5. Apply for AI and ML jobs. So far we’ve just talked about passive ways to get there. But you can also actively look for job postings on Indeed, apply and see what comes out of it. It doesn’t hurt, now, does it?


All that might sound overwhelming. Like you need to do all that just to land your most coveted job, after spending months studying?

Of course you might not have to do all of these to get there.

But I gave you all the options that you have if what you are doing still doesn’t work. If you do all of these, you’ll definitely land on something.

I think that’s it with this guide on how to get started learning artificial intelligence in 2021 for complete beginners.

Don’t forget to leave your thoughts about this approach to learning artificial intelligence from scratch in the comments section below.

Why learn artificial intelligence?

To finish this guide on getting started with artificial intelligence, let me share with you 3 reasons why I think learning artificial intelligence in 2021 is a great idea.

1. It is the skill of the century

Let’s bust this myth that AI is set to replace all jobs done by humans and then destroy humanity in one blow.

That couldn’t be further from the truth.

While it’s true that AI is going to move some jobs obsolete, it is an emerging technology that is making a dent in almost every industry ranging from fashion to finance. It is even projected to create more than 130 millions jobs in all sectors of the economy.

So by learning artificial intelligence you get to be part of this transformation.

2. Bright career prospects and pay

How about you follow the money?

If the fact that AI is a trending skill to learn in 2021 is not good enough, then another reason you might want to consider learning AI should because of the bright future it opens up for you careerwise.

Since AI applications span almost all sectors, the demand for AI engineers has really skyrocketed. It means a lot of companies are hiring. Roles that are open include data scientist, machine learning engineer, business intelligence developer etc.

Besides, unlike other sectors, pursuing a career in artificial intelligence grants you a fat annual paycheck ranging from USD $ 100,000 to 150,000 in the United States.

3. Handle big data and some more

How about the fun of just working with a colossal amount of data?

Look, it’s not the 19th century anymore (as if I know anything about the 19th century).

But we humans generate cosmic amounts of data, amounting to 2.5 quintillion bytes per day. And no I didn’t randomly pick that figure from my head. Here is my source.

Well, who is going to handle all this data and make sense of it? It’s you. The data scientist, machine learning engineer, artificial intelligence developer… whatever term suits you.

By learning artificial intelligence in 2021, you’ll be able to take this data, feed it into machine learning algorithms and retrieve behavioural patterns.

These patterns from consumers can transform into very useful insights for business to turn into profit or for governments to protect themselves from a potential breach that could bring down an entire civilisation.

You get the idea of the immense role AI is going to play in the near future?


I hope this guide on how to learn artificial intelligence in 2021 has provided you with a blueprint to follow in order to launch a successful career in AI.

It goes without saying that everyone has their own approach when it comes to how to learn artificial intelligence from scratch… this is just the method that worked for me.

The bottom line is, if you want to get started in artificial intelligence, start.

It doesn’t matter from where.

Most of these things you get to figure out along the way. 

Are you a budding artificial intelligence engineer or you are already experienced in this field for a couple of years?

What do you think are some of the best ways to get started learning artificial intelligence from scratch,hat I did not mention in this list?

Please share your thoughts and experiences in the comments below.

2 thoughts on “How To Learn Artificial Intelligence In 2021 [Complete Guide]”

  1. Sounds very practical and thanks for writing this blog! If I decide to try it, I’ll keep you updated about how far /where I go at the end.

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