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 [email protected]

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:

Table of Contents

• Start with short YouTube video introductions
• Follow free online courses on YouTube
• Read articles
• Read books
• No math background for ML? Check this out!
• No coding background, no problem
• Follow online courses
• Practice, practice, and practice!
• More Resources (Communities, cheat sheets, news, and more!)

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.

• Introduction to the most used terms

  • Learn the basics in a minute - What's AI - YouTube Playlist

• Understand the neural networks

  • Neural Networks Demystified - Welch Labs - YouTube Playlist
  • Learn Neural Networks - 3Blue1Brown - YouTube Playlist

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.

• Introduction to machine learning - YouTube Playlist (Stanford)

• Introduction to deep learning - YouTube Playlist (MIT)

• Deep learning specialization - YouTube Playlist (Deeplearning.ai)

• Deep Learning (with PyTorch) - YouTube Playlist (Yann LeCun)

Read articles

Read many articles

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

• Start Machine Learning in 2021 - Become an expert for free! - Louis Bouchard
• 5 Beginner Friendly Steps to Learn Machine Learning and Data Science with Python - Daniel Bourke
• What is Machine Learning? - Roberto Iriondo
• Machine Learning for Beginners: An Introduction to Neural Networks - Victor Zhou
• A Beginners Guide to Neural Networks - Thomas Davis
• Understanding Neural Networks - Prince Canuma
• Reading lists for new MILA students - Anonymous
• The 80/20 AI Reading List - Vishal Maini

Read Books

Read some books

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

• Deep learning book - Free Online
• Dive into Deep Learning - Free Online
• Probabilistic Machine Learning: An Introduction - Free Online
• Artificial Intelligence: A Modern Approach - Optional (Paying)
• Pattern Recognition and Machine Learning - Optional (Paying)
• Deep Learning with Python - Optional (Paying)
• Understanding Machine Learning: From Theory to Algorithms - Shai Shalev-Shwartz and Shai Ben-David - Free Online

Great books for building your math background:

• Mathematics for Machine Learning - Free Online
• The Elements of Statistical Learning - Optional (Paying)
• Statistical Inference - Optional (Paying)

A complete Calculus background:

• Calculus: Concepts and Contexts - Optional (Paying)
• Single Variable Calculus: Concepts and Contexts - Optional (Paying)
• Multivariable Calculus: Concepts and Contexts - Optional (Paying)

These books are completely optional, but they will provide you a better understanding of the theory and even teach you some stuff about coding your neural networks!

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):

• Linear Algebra - Khan Academy
• Statistics and probability - Khan Academy
• Multivariable Calculus - Khan Academy

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

• mathematicalmonk - YouTube
• Mathematics for Machine Learning - Garrett Thomas
• An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) - Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

If you still lack mathematical confidence, check out the Read books section above, where I shared many great books to build a strong mathematical background. 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.

• Practical Machine Learning Tutorial with Python - Free YouTube python introduction
• Learn Python - Free interactive tutorial to learn python
• Learn Python Basics for Data Analysis - Free course on OpenClassrooms
• Machine Learning with Python | Coursera - IBM - Optional (Paying)
• Introduction to Python for Data Science - In this Python for Data Science course, students will be learning core Python concepts and use the language as it relates to data science in a 16-week learning program (paying, optional).
• 100 numpy exercises - A collection of exercises that have been collected in the numpy mailing list, on stack overflow and in the numpy documentation.

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.

• DEEP LEARNING - Yann LeCun - This course concerns the latest techniques in deep learning and representation learning. - Free
• Intro to Machine Learning - Kaggle - Learn the core ideas in machine learning, and build your first models. - Free
• Get started in AI / AI For everyone - Andrew Ng - Paying, optional
• Machine learning - Andrew Ng - Stanford - Paying, optional
• Deep learning specialization - Andrew Ng - Paying, optional
• TensorFlow (Professional certificates) - Paying, optional
• AI Engineering - IBM (Professional certificates) - Paying, optional
• Complete data science bootcamp 2020 - Paying, optional
• Machine learning - No coding - Paying, optional
• Data Science Training + Industry Experience - A complete instructor-led 16-week training program with experience (paying, optional).
• Instructor-led Online Data Science Bootcamp - A complete instructor-led 16-week learning program (paying, optional).
• fast.ai's Deep Learning Courses - Free

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!

• A Discord server with many AI enthusiasts - Learn together, ask questions, find kaggle teammates, share your projects, and more.

• A Discord server where you can stay up-to-date with the latest AI news - Stay up-to-date with the latest AI news, ask questions, share your projects, and much more.

• Follow reddit communities - Ask questions, share your projects, follow news, and more.

  • artificial - Artificial Intelligence
  • MachineLearning - Machine Learning (Biggest subreddit of the field)
  • DeepLearningPapers - Deep Learning Papers
  • ComputerVision - Extracting useful information from images and videos
  • learnmachinelearning - Learn Machine Learning
  • ArtificialInteligence - AI
  • LatsestInML - Game-changing developments in machine learning you shouldn't miss

Save Cheat Sheets!

• The best Cheat Sheets for Artificial Intelligence, Machine Learning, and Python.
  • Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data - Stefan Kojouharov
  • Machine Learning cheatsheets for Stanford's CS 229 - Afshine Amidi & Shervine Amidi
  • Cheat Sheet of Machine Learning and Python (and Math) Cheat Sheets - Robbie Allen
  • AI Expert Roadmap - Use it as a skillset checklist!

Follow the news in the field!

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

  • What's AI - Weekly videos covering new papers
  • Two Minutes Papers - Bi-weekly videos covering new papers
  • Bycloud - Weekly videos covering new papers

• LinkedIn Groups

  • Artificial Intelligence, Machine Learning and Deep Learning News - News of the field shared by everyone in the group
  • Artificial Intelligence | Deep Learning | Machine Learning
  • Applied Artificial Intelligence

• 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

  • BAILOOL/DoYouEvenLearn - Essential Guide to keep up with AI/ML/DL/CV

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

If you'd like to support me, I have a Patreon where you can do that. Thank you, and let me know if I missed any good resources!

This guide is still regularly updated.