Start AI in 2021 — Become an expert from nothing, 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!
I’m asked the same question multiple times a day on my social media. The question is “How can I start in machine learning?”. It frequently takes multiple forms, such as “How can I start for free?” or “How can I start if I don’t have a developer background”, etc. So I decided to write a complete guide on how to start in machine learning in 2021 from no background at all, and for free. Because of these pertinent questions, I’ve researched a lot of resources and saved the best ones on a notepad over the past year to quickly answer the next upcoming questions.
Today, I will share this notepad with everyone and list many great resources, and give you some tips on how to learn, and improve your machine learning skills.
Who can become a machine learning expert in 2021?
This guide is intended for anyone having zero or a small background in programming, mathematics, and/or machine learning. There is no specific order to follow, but a classic path would be from top to bottom, following the order given in this article. If you don’t like reading books, skip the section, if you don’t want to follow an online course, you can skip this one as well. There is not a single way to become a machine learning expert, and with motivation, you can absolutely achieve it by creating your own steps.
But the goal of this article is to give a path for anyone wanting to get into machine learning and not knowing where to start. I know it can be hard to find where to start, or just what to do next when learning something new. Especially when you don’t have a teacher or someone to guide you. This is why I will list many important resources to consult ordered by “difficulty” with a linear learning curve. If you are more advanced, you can just skip some steps.
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 may be useful to others as well.
Don’t be afraid to replay videos or learn the same concepts from multiple sources. Repetition is the key to success in learning something new!
Table of contents
Here is a quick table of contents of this article if you would like to skip the first steps right away:
— Let’s dive in! (Start with short YouTube video introductions)
— Start learning seriously (Follow free online courses on YouTube)
— Read articles online
— Read important books
— No math background for ML? Check this out!
— No coding background, no problem! (Coding resources for beginners)
— Follow online courses
— Practice, practice, and practice!
— More resources (join communities, use cheat sheets, follow news of the field, and more!)
Note that there is also a repository of this article with all the resources clearly identified for you to follow in order as well.
Let’s dive in!
In my opinion, the best way to start learning anything is with short YouTube video introductions. This field is no exception. There are thousands of amazing videos and playlists that teach important concepts of machine learning for free on this platform, and you should definitely take advantage of them.
Here, I list a few of the best videos I found that will give you a great first introduction to the terms you need to know to get started in the field.
The first one I would personally recommend is a YouTube Playlist introducing the most used terms of the field by What’s AI. This is a must-watch playlist to have a basic understanding of machine learning and all the terms frequently said in the further resources mentioned. It’s just a series of really short 1-minute videos covering all the most used terms starting from the basics to more advanced. It will take no longer than 30 minutes to have a complete overview of the field!
Following these short videos, I would suggest diving a little deeper into the foundations of machine learning and deep learning and learn more about neural networks. Understanding neural networks and backpropagation is the most important thing when starting and gives you an enormous advantage when you dive into more advanced lectures and courses.
Fortunately for us, an amazing channel called 3Blue1Brown created a series of videos covering these exact concepts really clearly. Don’t be afraid to replay the videos or find any other playlist on your own if certain aspects of the videos are unclear to you! Learning is made of repetition!
Now that you have a good basis of what a machine learning algorithm is, how it works, and how it can learn with backpropagation, you are ready to dive a little deeper with more complete and advanced courses.
Feel free to keep on watching other videos on YouTube. It has great resources and is a free, and underrated platform for learning!
Start learning seriously
This step is a little longer than the previous one since you will be watching many hours of free, amazing courses on YouTube, and learn a lot from them.
Please, do not watch these courses while doing something else. They are great resources that deserve concentration and you take notes and ask questions through online communities!
They are fascinating as well. Once you have the motivation to press the “Play” button, you will be hooked until the end and learn A LOT, I promise!
Here’s a quick list of the best courses I would suggest watching first. They are listed in the“difficulty of understanding” order. Starting from an introduction and ending with a great specialization. Note that even the specialization is still an introduction at this point, but it will prepare you for the funnier stuff coming next! Of course, they are all free!
Read articles online
As it has been proven multiple times. Humans learn better by repeating and learning in different ways, such as hearing, writing, reading, watching, etc. This is why it is as important to read as to watch videos to have a better understanding.
You will cover many angles and have a more complete view of what you are trying to learn. This section is a list of short articles that are completely free and optional.
Note that you can certainly find other items on your own, these are only suggestions based on my personal choices.
Here are 5 articles, all available here on Medium, that I would suggest you look at before diving into any books, coding, or online courses. They are all short reads really beneficial if you couple that with the videos mentioned earlier.
- 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
Now that you’ve gone through these short reads and videos, you are definitely ready to start coding and practice! If you feel like you need a deeper understanding of the theoretical aspect of machine learning, then, the next section is for you. Otherwise, you can skip straight to the coding, online courses, or practice sections below to learn exponentially fast!
Read Important Books
This section is completely optional, but it is strongly recommended to gain a better understanding of the “behind the scenes” of a machine learning algorithm. Books are a great way to learn at your rhythm. Be sure to understand everything before going into practice mode. Don’t be afraid to re-read sections!
As you may know, most books need a lot of work from the authors, and therefore, are not free. Fortunately for us, there is one amazing book that is completely free and available online! The rest are available on Amazon for buying. Here I list some of the best 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)
Again, 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!
Now, you are more than ready to start coding and apply the theory you’ve learned and mastered.
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 on 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 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!
This section is for beginners in coding. If you have no background at all in Python or any other programming languages, this will get you starting and give you an awesome basis for machine learning programming.
If you are already pretty familiar with python, you can skip to the “Follow online courses” step!
Here are the best online courses to learn the programming side of machine learning:
Following these two resources, if you are still not comfortable with python, you can dive deeper into a paying course on Coursera by IBM called Machine Learning with Python. This will certainly get you ready to start coding your first machine learning algorithms and dramatically improve your programming skills! Another great idea is to follow online training, like this one by Data Science dojo. Note that these are often paying and completely optional. They are just other resources for those of you that would like to have more “guided” practices using live sessions where they will give you challenging exercises and projects to work on! An alternative could be to find exercises and projects online, using Kaggle for example, join a community to find learning teammates and ask questions.
Confirm your python skills with these 100 NumPy exercises. It is a great collection of exercises that have been collected in the NumPy mailing list, on stack overflow, and in the NumPy documentation.
Follow Online Courses
Now that you have a good understanding of the theory behind machine learning AND a coding background, you are ready to start your way into machine learning courses. Of course, these are all optional. Again, the first one is free and the other ones are paying since they will teach you many things and some even give you certifications you can use in your resume!
If you don’t want to follow any courses, you can jump to the next section and start to practice on your own. It will be a little more difficult at first, but with your “googling” skills and motivation, you will be able to do this for sure.
If you prefer to be more guided and have clear steps to follow, these courses are the best ones to do (starting from the basics to more advanced):
- Intro to Machine Learning — Kaggle (Learn the core ideas in machine learning, and build your first models)
- Get started in AI / AI For everyone — Andrew Ng
- Machine learning — Andrew Ng — Stanford
- Deep learning specialization — Andrew Ng
- TensorFlow (Professional certificates)
- AI Engineering — IBM (Professional certificates)
- Complete data science boot camp 2021
- Instructor-led Online Data Science Bootcamp — datasciencedojo (complete 16-week learning program)
- Data Science Training + Industry Experience — datasciencedojo (complete 16-week training program with experience)
- fast.ai’s Deep Learning Courses — Free
Now, you are more than ready to start to practice and build your portfolio!
Practice, practice, and practice!
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. You have to find a problem to solve before you can even start coding, which can be very difficult without any help.
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 learning alone. The following section is devoted to this.
Most of the time, the best way to learn is to learn with someone else. Join communities online and find partners to learn with!
— Here is an awesome Discord Server with many AI enthusiasts where you can learn together, ask questions, find Kaggle teammates, share your projects, and much more.
— Here is a great Discord Server where you can stay up-to-date with the latest AI news, ask questions, share your projects, and much more.
— You can also follow Reddit communities — Ask questions, share your projects, follow news of the field, and more. Here are the most popular ones:
- 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!
— Another important thing in this field is to stay up to date with the new upcoming papers and new applications that are released every single day. A great way is to join LinkedIn groups that are sharing these new applications, follow medium publications or even YouTube channels that are summarizing these new papers, here I list a few of the best ones I know, but you can definitely search for more in your fields of interest!
Subscribe to YouTube channels that share new papers: Stay up to date with the news in the field!
- Two Minutes Papers — Biweekly videos covering new papers
- What’s AI — Weekly videos covering new papers
- Bycloud — Weekly videos covering new papers
— Join LinkedIn Groups
- Artificial Intelligence, Machine Learning, and Deep Learning News
- Artificial Intelligence | Deep Learning | Machine Learning
- Applied Artificial Intelligence
— Join 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.
— Subscribe to 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 - Covering news in AI
— 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.”
- A little self-promotion, myself — “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
This was a guide for anyone having zero or a small background in programming, mathematics, and/or machine learning. There is no specific order to follow, but a classic path would be from top to bottom.
Note that this is a non-exhaustive list of resources to become a machine learning expert from nothing in 2021. You can definitely use more, or less, resources and learn at your rhythm. Not everything in life is a competition and you must follow your instinct in the best way you can learn. Don’t ever feel guilty about replaying a video or reading an article twice to understand a concept. We’ve all been through this and it is perfectly normal. The most important thing is that you understand the concepts, and not that you go through the list as quickly as possible.
Thank you so much for reading, and I wish you all the best of luck in your future machine learning careers!
— Signing out.
Here’s a GitHub repository with all the links in this article if you would like to contribute or just easily find the information through it:
Come chat with us in our Discord community: Learn AI Together and share your projects, papers, best courses, find Kaggle teammates, and much more!