How To Learn Machine Learning In 7 Simple Steps

How To Learn Machine Learning In 2021 In 7 Simple Steps

What is machine learning?

If you’ve been interested in a career in tech then you probably have heard about terms like machine learning, deep learning and artificial intelligence being thrown left, right and center.

In particular are the tons of machine learning job ads that are all over the place.

So you probably got interested but are still in the dark as to what  machine learning actually means and how to get started learning machine learning.

(Tongue twister there yeah?)

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Well, in this article we are going to define what machine learning is.

I am also going to outline a simple 7 step process to follow for you to get started with machine learning from scratch and finally land your first job as a machine learning engineer.

First let’s begin by defining machine learning.

What is machine learning?

Machine learning is a branch of artificial intelligence that enables us to build applications that are able to automatically learn from experience or data and improve their accuracy without necessarily being programmed to do so.

This is particularly possible because machine learning algorithms are trained to look for patterns in data and use it to make decisions or predictions without human intervention.

Now we know what machine learning is.

But before we get down to the steps you need to take to get started learning machine learning, let’s first look at 3 reasons why you should learn machine learning in 2021.

Why learn machine learning?

So, what’s the buzz all about and why should you care?

Well, here are 3 reasons why I think machine learning should be on your bucket list of important skills to pick in 2021 if you are interested in working in tech.

1. Machine learning experts are in high demand

Machine learning is a skill that is in very high demand today.

This is thanks to more and more companies now applying artificial intelligence into their product development and processes.

Think of sectors like cybersecurity, image recognition, medicine, banking, e-commerce and video streaming platforms all looking for machine learning professionals to develop artificial intelligence applications for their lines of products.

With skills in machine learning, you are opening yourself to a career with endless job opportunities that go unfilled for the most part of the year.

In addition to that, most small businesses have also realized how business intelligence is deeply influenced by machine learning, and so are willing to heavily invest in it in order to stay ahead of the competition.

2. Machine learning experts earn top dollar

If you are looking for a career in tech where you can earn some of the highest salaries then you should think about machine learning.

As a machine learning engineer, you are looking at an annual salary of USD $ 142,000 on average, with a machine learning expert looking at salaries of up to USD $ 195,000 per year.

Now if you are an experienced software engineer, and I am talking more than 10 years of experience, you’d be hard-pressed to pull in anything near the average annual salary for a machine learning engineer.

So if the passion for developing algorithms and neural networks is not strong enough to pull you in, how about you follow the money?

3. Machine learning is linked to data science

You probably hear more of data science than machine learning.

If you want to acquire a skill that is niche but still allows you to work in the big and broader data science field then I strongly recommend learning machine learning.

And since data science has been rated as the sexiest job in the 21st century, you can start your career as a data science expert, by specializing in machine learning, as you’ll often need to liaise with the data science engineers in your firm to synchronize your workflows.

So by getting started with machine learning you’ll become competent in both of these fields, which enables you to analyze surprisingly huge data sets, derive value from them and then use that to provide valuable insights.

I hope these 3 reasons got you charged enough to want to get started learning machine learning from scratch in 2021.

Now, without much further ado, let’s get into the step by step guide on how to get started with machine learning today.

And in this guide, I focus on a self-study approach. Not a college degree or university course. So you’ll pretty much be in control of your learning while also being able to optimize your learning in a way that works best for you.

How to learn machine learning?

1. Learn Calculus

I know, I know.

There are guys out there who would tell you that you don’t need a course in these theoretical skills if you want to get started with machine learning.

But how exactly do you do that? Because I can tell you from experience that getting started learning machine learning can be a nightmare, if not downright intimidating if you don’t first get a gentle introduction to its prerequisites.

Now I am not saying you go bang your head until you become a professional mathematician with top-notch skills in calculus.

But to get started with machine learning today, you need to have the core skills in calculus nailed.

Most machine learning applications tie back into learning the underlying distribution of data which again ties directly to probability. Once you get a grasp of this distribution, you can accomplish a huge range of tasks.

So learning calculus is inevitably key to unlock the superpowers of probability and statistics.

And for this, there are a ton of free tutorials online that will give you a head start. And while at it, ensure that you actually work along with the practice problems. Don’t just nod your head along with the instructor, as this way you’ll end up not learning a thing.

2. Learn Algebra

The next theoretical skill I believe you need to pick up when getting started learning machine learning in 2021 is to learn some algebra.

And in this case, I am talking about linear algebra that is particular geared towards students of machine learning.

Again when it comes to algebra the key is to soak as much theory as you can from it by practising along using the practice problems that most tutorials and courses usually provide.

Just like the previous part on learning calculus, you might be tempted to just skimp on this part so that you can get your hands dirty with code as fast as you can, but I strongly encourage you to take your time.


Because once you fulfil the basic prerequisites or these requirements, the rest will for sure be pretty easy as most machine learning development involves applying concepts from statistics and computer science to data.

While some might suggest that you just skip calculus and algebra altogether and wing your way through machine learning by trial and error, I pose the same question again?

Why go the long way, when you can take a shorter route by first dabbling with the basics and saving yourself the trouble later?

3. Learn Python

Now the next step to take when it comes to learning machine learning is to learn to code.

No really!

There is no going around this one if you are really serious about a career in ML.

And for this, I strongly recommend that you get started by learning Python programming. Now, I know this might sound a little biased, as there are a ton of other languages that are equally suitable for ML but let me explain.

It is true that you can do machine learning in many other languages out there, but Python is kind of the golden standard when it comes down to it.

It is super easy to pick up, there are a ton of machine learning libraries, ready-made.. just waiting for you to tap into their potential and use them to develop artificial intelligence applications.

With Python, you’ll be able to integrate good programming best practices, make modular and readable code, write proper unit tests as well as handle errors.

Well, that can be said of other languages too, right? 

I guess every developer has a bias towards their favourite programming language, and you shouldn’t be crucified for that.

In order to start another cold war on programming languages, let me underscore that if you are already productive in another programming language like Java, then go ahead and use it to learn ML. 

Otherwise, check out my other article on the best programming languages for data science in 2021.

If you are a beginner learning to code from scratch, my advice is not to try to memorize all commands of the programming language by heart. Because you can’t. Just be a pro at asking questions on and then sifting through the answers to find what could be what you are looking for 🙂

4. Learn Machine Learning


Enough with dancing around laying the foundation. It’s now time to dive right into machine learning theory and practise with both feet.

And by this, I mean going all-in into the abstractions in machine learning. Learning how to use them to drive your approach to data, models, algorithms etc.

You want to look at all topics that underlie machine learning methods, as well as a practical approach on how to get started using machine learning. This is the fun part. These are the skills that you’ll later apply in self-driving cars, AI assistants, detecting fraud, spam among others.

Again, this stage of learning machine learning is about absorbing and soaking up as much theory and knowledge as you can.

I have heard some people suggest that you could still skip this theoretical part and jump right away into using the ready-made packages and hit the ground running…

Here’s my argument against that.

You’ll fail at step 5 of this process of learning data science from scratch. 


Because you won’t be able to build meaningful personal projects if you don’t know the theory behind data collection, preprocessing and driving business value. You’ll then struggle to convince hiring managers to hire you (aka give you a shot). That is to say, you’ll be out of a job, despite the high demand for ML engineers.

But again I am not saying that you hold your breath until you have all answers to all these questions from the start.

The principle is to learn as much theory as is enough to get you started and not go astray. Mastery then comes with time and you dabble between theory and practice. And for this, I think this Starford university ML course on YouTube will definitely get the job done.

I know there are great books out there for learning machine learning from scratch but I find videos more interactive and easier to follow than books.

However, I still keep some books handy for reference purposes like the Elements of Statistical Learning again from Stanford University.

It might sound boring that we are still talking about theory at this stage, but it’s not exactly the case.

These machine learning resources will give you a lot of practice problems to solve.. .guided of course. So you are able to learn theory, try them out, learn some more theory, practice, until the end of the course.

After this, you are now ready to dive into your own personal deliberate projects to hone your skills. For this check out the next step in learning machine learning from scratch.

5. Build Projects

Okay, now you have read and studied. 

So you have all the basics nailed. But reading and studying can only take you so far when it comes to getting started with machine learning.

In order to demonstrate your actual machine learning skills, you need to put them into a project that you can demonstrate, say before a panel of interviewers when you are looking for a job.

So after learning all the tools and theory that you need, you now have to get your hands dirty with some real data and implement models. And the best way to get started is to look for Python machine learning projects for beginners.

And when I say you build your personal projects, I don’t mean you go take the most popular and well-known algorithms and apply them to well-known datasets.

Because everyone has done that. 

All hiring managers already know about these algorithms and datasets. Besides, by building on these, you won’t learn how to do anything else outside these datasets. As you don’t get to push yourself and be creative.

Here are 3 tips that I have for you when building your portfolio while practising with personal projects:

  1. In your projects try to practice the entire machine learning workflow. By this I mean, start all the way from data collection, data cleaning and preprocessing, all the way to model building tuning and evaluation.
  2. Integrate the use of real datasets in your projects as opposed to dummy datasets. By doing this repeatedly, you’ll be able to build your own intuition around the kinds of models that are suitable for certain challenges.
  3. Dive deeper into individual topics. In the previous step on the ML theory, you learnt about advanced techniques like classification & dimensionality reduction. Now apply different clustering algorithms to your datasets to see which one performs better.

So build projects where you get to learn how to prepare your own data as well as machine learning DevOps.

With DevOps, you’ll learn how to deploy your models so that they can run in the cloud, which is a key skill to have. Familiarize yourself with the basics of cloud computing for machine learning applications.

This way, you’ll stand out from the crowd and easily land your coveted ML job.

6. Network & Network

Your main objective of getting started with machine learning is to finally land a meaningful job with a reputable company, right?

Well, then you have to keep this in mind at all stages of your learning process.

Not unless you are just doing this as a hobby, in which case skip this part.

The challenge that most beginners who are getting into machine learning face, is getting their first real job after learning the basics of machine learning, and a few personal projects under their belts.

According to them, it should be easy to get that coveted machine learning gig after they master these skills.

Unfortunately, this is now how it all goes down.

In the real world, you have to talk to people. If you are on professional networking sites like LinkedIn then you need to make connections with other machine learning experts, as these are ways to put yourself in front of the right people.

But most importantly, if you want to get started in machine learning, attend machine learning events. Participate in competitions like those organized by Kaggle.

These often provide you with opportunities to demonstrate your amazing ML skills, which could be through doing presentations, participating in competitions, winning awards etc.

7. Ask For Help

Okay, now to the last step in getting started with a career in machine learning.

One important thing that I learnt the hard way as a budding machine learning engineer is to know when to ask for help.

While this is something that I might have been clear from my previous steps, I wanted to give a special section so that you cannot ignore it.

In learning machine learning in 2021, you have to know when to ask for help.

You don’t have to know all the algorithms off head and memorize all the Python commands in order to prove your point as a genius data scientist. There are times when I was stuck for days trying to fix one algorithm bug on my own.

Later, I realized I could have saved myself the trouble for days by just Googling the question. There was already an answer to it. Can you imagine?

Join machine learning forums, StackOverflow channels… anywhere where you can interact with other ML engineers and get your questions answered. It will save you many a headache.


The experts do this too.


I hope this guide on how to get started with machine learning has opened your eyes to the exact steps you need to take in order to learn machine learning.

Machine learning is one of the trending career fields in technology in 2021, with an opportunity to work with some amazing companies as well as work on some amazing projects.

Just to recap, here are 3 reasons why I think you should get started learning machine learning today.

  • It is a very high demand skill. Machine learning developers are needed by most companies that are trying to stay relevant today. So it will be quite easy to get your first real job.
  • It pays some of the best salaries in the tech industry. If you compare machine learning experts and software engineers, the difference in their salaries is like day and night. You want to be part of the machine learning team.
  • It prepares you for a career in data science. Since machine learning is a subsection of data science, picking skills in machine learning directly gives you an opportunity to work as a data scientist… the sexiest job in the 21st century.

If you are looking for a field in tech that will offer you a great degree of job satisfaction then you should get started learning machine learning. That is not to say that it is an extremely easy field to get into…

Or that ML is easy to learn. Not by a long shot!

Anything that is worth its salt requires some degree of hard work and dedication upfront.

Are you a machine learning expert or are looking to get started in machine learning?

What are some of the resources that are great for learning machine learning that I didn’t mention in this list but are super important?

Please share your thoughts in the comments below.

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