March 26, 2021

1897 words 9 mins read



A complete guide to start and improve in machine learning (ML), artificial intelligence (AI) in 2021 without ANY background in the field and stay up-to-date with the latest news and state-of-the-art techniques!

repo name louisfb01/start-machine-learning-in-2020
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created 2020-09-24
license MIT License

Start Machine Learning in 2021 - Become an expert for free!

A complete guide to start and improve in machine learning (ML), artificial intelligence (AI) in 2021 without ANY background in the field and stay up-to-date with the latest news and state-of-the-art techniques!

This guide is intended for anyone having zero or a small background in programming, maths, and machine learning. There is no specific order to follow, but a classic path would be from top to bottom. If you don’t like reading books, skip it, if you don’t want to follow an online course, you can skip it as well. There is not a single way to become a machine learning expert and with motivation, you can absolutely achieve it.

All resources listed here are free, except some online courses and books, which are certainly recommended for a better understanding, but it is definitely possible to become an expert without them, with a little more time spent on online readings, videos and practice. When it comes to paying courses, the links in this guide are affiliated links. Please, use them if you feel like following a course as it will support me. Thank you, and have fun learning! Remember, this is completely up to you and not necessary. I felt like it was useful to me and maybe useful to others as well.

Don’t be afraid to repeat videos or learn from multiple sources. Repetition is the key of success to learning!

Maintainer - louisfb01

Feel free to message me any great resources to add to this repository on

Tag me on Twitter @Whats_AI or LinkedIn @Louis (What’s AI) Bouchard if you share the list!

Want to know what is this guide about? Watch this video:

Watch the video

Table of Contents

Start with short YouTube video introductions

Start with short YouTube videos introductions

This is the best way to start from nothing in my opinion. Here, I list a few of the best videos I found that will give you a great first introduction of the terms you need to know to get started in the field.

Follow free online courses on YouTube

Follow free online courses on YouTube

Here is a list of awesome courses available on YouTube that you should definitely follow and are 100% free.

Read articles

Read many articles

Here is a list of awesome articles available online that you should definitely read and are 100% free.

Read Books

Read some books

Here are some great books to read for the people preferring the reading path.

No math background for ML? Check this out!

No math background for ML? Check this out!

Don’t stress, just like most of the things in life, you can learn maths! Here are some great beginner and advanced resources to get into machine learning maths. I would suggest starting with these three very important concepts in machine learning (here are 3 awesome free courses available on Khan Academy):

Here are some great free books and videos that might help you learn in a more “structured approach”:

You now have a very good math background for machine learning and you are ready to dive in deeper!

No coding background, no problem

No coding background, no problem

Here is a list of some great courses to learn the programming side of machine learning.

Follow online courses

(Optional) Get a better understanding and more guided practice by following some online courses

If you prefer to be more guided and have clear steps to follow, these courses are the best ones to do.

Practice, practice, and practice!

Practice is key

The most important thing in programming is practice. And this applies to machine learning too. It can be hard to find a personal project to practice.

Fortunately, Kaggle exists. This website is full of free courses, tutorials and competitions. You can join competitions for free and just download their data, read about their problem and start coding and testing right away! You can even earn money from winning competitions and it is a great thing to have on your resume. This may be the best way to get experience while learning a lot and even earn money!

You can also create teams for kaggle competition and learn with people! I suggest you join a community to find a team and learn with others, it is always better than alone. Check out the next section for that.

More Resources

Join communities!

Save Cheat Sheets!

Follow the news in the field!

  • Subscribe to YouTube channels that share new papers - Stay up to date with the news in the field!

  • LinkedIn Groups

  • Facebook Groups

    • Artificial Intelligence & Deep Learning - The definitive and most active FB Group on A.I., Neural Networks and Deep Learning. All things new and interesting on the frontier of A.I. and Deep Learning. Neural networks will redefine what it means to be a smart machine in the years to come.
    • Deep learning - Nowadays society tends to be soft and automated evolving into the 4th industrial revolution, which consequently drives the constituents into the swirl of societal upheaval. To survive or take a lead one is supposed to be equipped with associated tools. Machine is becoming smarter and more intelligent. Machine learning is inescapable skill and it requires people to be familiar with. This group is for these people who are interest in the development of their talents to fit in.
  • Newsletters

    • Synced AI TECHNOLOGY & INDUSTRY REVIEW - China’s leading media & information provider for AI & Machine Learning.
    • Inside AI - A daily roundup of stories and commentary on Artificial Intelligence, Robotics, and Neurotechnology.
    • AI Weekly - A weekly collection of AI News and resources on Artificial Intelligence and Machine Learning.
    • AI Ethics Weekly - The latest updates in AI Ethics delivered to your inbox every week.
    • What’s AI Weekly - The latest updates in AI explained every week.
  • Follow Medium accounts and publications

    • Towards Data Science - “Sharing concepts, ideas, and codes”
    • Towards AI - “The Best of Tech, Science, and Engineering.”
    • OneZero - “The undercurrents of the future. A Medium publication about tech and science.”
    • What’s AI - “Hi, I am Louis (loo·ee, French pronunciation), from Montreal, Canada, also known as “What’s AI”. I try to share and explain artificial intelligence terms and news the best way I can for everyone. My goal is to demystify the AI “black box” for everyone and sensitize people about the risks of using it.”
  • Check this complete GitHub guide to keep up with AI News

Tag me on Twitter @Whats_AI or LinkedIn @Louis (What’s AI) Bouchard if you share the list!

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