Are you looking for the best resources for learning artificial intelligence?
Have you searched online and are overwhelmed by the immense amount of resources available, claiming to teach your artificial intelligence, machine learning and data science?
A search for AI learning resources often goes like this…
You hit Google, type ‘best ai resources’ you get 699,000,000 results returned. Each titled 101 best learning resources for data science, AI and ML. Now you don’t know which one to choose, or where to start from.
So what do you do?
Click close on the browse tab, pick up your remote and hit Netflix.
(Okay, I’m exaggerating but you get the point?)
Well, in this article, I am going to list some of the best resources for learning data science, machine learning and artificial intelligence.
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Instead of overwhelming you with 101 links, I’ll only provide what you need to get started. As you gain more knowledge of data science, it will be easier for you to know how to find resources for advanced learners.
Why do I say that?
Because most people who search for the best ai resources are beginners.
In this article, we’ll look at some FREE and PAID resources that you can use to quickly get started and launch a successful career in AI, ML and data science.
However, I will not detail how to use them, or any tips on how to become successful in this endeavour. It’s because these topics deserve more detailed and complete articles on their own. And I have already covered them before.
Here are the links if you want to check them out:
- How To Get Started Learning Artificial Intelligence
- How To Get Started Learning Machine Learning In 2021
- How To Become A Data Scientist Without A College Degree
Once, you read these complete getting started guides, you’ll be able to better make use of these resources and learn faster.
Okay, that is out of the way. Now, why not get right to it and list the best learning resources for data science, machine learning and artificial intelligence?
By the way, these terms data science, ML and AI are always thrown together, like they mean the same thing when they don’t. I will write another detailed article explaining the difference between these terms or fields. Then I’ll come back and update the link.
Above is how these three search terms have been trending in the last 5 years. Seems like their demand has been pretty steady.
Let’s get started.
You don’t just jump in machine learning and start ‘learning’. You’ve got to get ready first, and this you do by starting by learning math, probability and statistics.
This detailed algebra course from MIT is what you need if you want a thorough introduction to algebra that will be useful in data science. While being a basic subject on matrices and linear algebra, you’ll focus on systems of equations, determinants and similarities. Having these skills is key in data science, AI and ML.
2. Calculus for Beginners by 3Blue1Brow. If you like learning through YouTube, then you’ll definitely enjoy following along with this calculus playlist as well. By the time you complete this course, you’ll have mastered integrals, derivatives and the fundamental theorem of calculus. Better, it has subtitles in other languages, if English is not your first language.
3. Statistics and Probability by Khan Academy. A proper introduction to statistics is another very important skill you need if you want to get into AI, and that’s why I included it in this list of the best resources for AI. Through this course, you’ll learn how to analyze categorical data, display and compare quantitative data, model data distributions etc.
As much as I just want to lead you to Python tutorials for learning AI, ML and data science, the fact is that Python is not the only data science programming language.
So I’ll link to some of the best AI resources for learning the various programming languages that enable AI applications development.
Now, you’ll choose your favourite programming language and hit the ground running. That being said, a number of machine learning platforms like Kaggle only decide to teach Python.
4. Python for Beginners by Kaggle. In this Python training course that is targeting beginners, you’ll learn Python from the ground up without any specific applications in AI. The goal is to give you a strong foundation before you get your hands dirty code AI & ML applications. So you of course start from ‘hello world’ all the way to working with external libraries.
5. Intermediate Python by Udacity. As you can see, it is an intermediate course, so previous knowledge of Python is assumed. So if you are a complete beginner then start with the beginner course first. The aim of this course is to equip you with advanced Python skills for file classification, data mining etc.
6. Advanced Python for Developers by Luiz Eduardo Borges (It’s a BOOK). I choose to add this book to the best ML resources in 2021 because it really goes into depth with Python. So if you want to tinker with Python further after the basics, then this is the book for you. It will teach you how to use Python to create UIs, computer graphics and distributed systems.
Once you learn about the mathematics and coding skills you need, it is now time to dive right into data science.
And here there are an amount of theory that you must absorb first before you can reach the level where you know what you are doing with code.
If you try to rush it through, avoiding the theory party, you won’t be as efficient an AI developer as you would if you took your time with the theory.
Here the the course that will teach you both the theory and practice behind artificial intelligence.
These courses and training also have the practical aspects where you’ll be putting your skills to test, through sample projects and code along exercises.
7. Artificial Intelligence Course by Stanford. This list of the best data science resources cannot be complete without the mention of this amazing introduction to artificial intelligence course from Stanford Engineering school.
Through this FREE YouTube playlist, you’ll dive right into AI theory, ML linear classifiers, neural networks and reinforcement learning among others.
8. Machine Learning Crash Course by Google. This self-study course is geared towards beginners who want to become professionals in ML. It uses a series of video lectures to teach the theory, real-world case studies to bring theory into perspective and hands-on practice exercises to test your understanding.
9. Machine Learning by Andrew Ng on Coursera. In this comprehensive beginner course to ML, you’ll learn the most effective machine learning techniques in 2021. Along the way, you’ll also gain practical experience by experimenting with and getting them to work. This course covers both the theoretical and practical aspects of machine learning.
Now, if you’ve been learning from YouTube tutorials or any kind of self-study then you agree with me on one thing…
Self-study requires a tremendous amount of discipline to pull off.
More so when it comes to picking up super technical skills like data science, where one concept could take you weeks to lock-in.
Plus, when you pick free AI resources, the trade-off is the time you have to spend figuring things out on your own.
What if you could take a guided crash course or training where the instructor is actually present to guide you through the way and answer any questions you have? What amount of times and brain bandwidth could be saved?
Enter boot camps.
These are an excellent alternative data science learning resource if you don’t want to brave this journey all alone.
So here are three well-known data science bootcamps that will help you launch a successful career as a data scientist, machine learning engineer or AI engineer.
10. Dataquest.io If you religiously follow the entire curriculum of Dataquest bootcamp, you’ll acquire all the skills you need to land your first data scientist job. I have included in this list of the best resources for data science because their 24-week program is strictly project-based. It’s a hands on approach to teaching. And most people learn by doing.
11. Springboard.com Now this is one Bootcamp that has a very interesting offering. If you take their data science program, you are guaranteed a job or your money back. Apart from being assigned a personal mentor and career coach, this track equips you with a data science ready profile, at the end, which opens you up to a network of data science jobs.
12. Metis.com (thisismetis.com) What I liked most about this bootcamp is that they offered in-person training and career mentoring in data science. However, since the pandemic, they’ve scraped off the in-person training and moved online. However, just like the other two programs, their online program is still project-based and gives you full immersion into data and analytics.
Okay, I know there are already enough resources to get anyone serious about a career in data science and artificial intelligence a great head start.
What if you want to take it further and stretch your limits?
For this I recommend a number of machine learning resources online that will provide you with a lot of practice, while also learning other soft skills that are key to your career success.
And for this there are two, find super-tough problems and solve them or join a competition and learn to work with a team on solving a particular problem. All the courses you took in machine learning, AI and data science will not teach you about team collaboration and comm skills.
But working in a team will force you to leave your ego at the door and accommodate others. Network with like-minded professionals and get exposed for your skills, which could lead to a job down the line.
Wait… you could even win MONEY!
13. Kaggle I think Kaggle is the most popular platform where you’ll find data science, machine learning and artificial intelligence competitions in 2021. With over 1 million registered users, ranging from beginners to season AI experts, this platform attracts new ML competitions. In fact, every other day you’ll find a new competition posted.
14. DrivenData.org Just like Kaggle, DrivenData also gives you an opportunity to either join a competition or host your own. If you are looking for an opportunity to use AI to build cutting edge predictive models that companies need to tackle 21st-century problems, then this is the place to be. Projects focus on health, education, development among others.
15. Codalab.org Last in this list of the ML competitions is Codalad. It is a web-based open-source platform that enables data scientists, researchers and developers to collaborate on ML and AI projects. You’ll be able to join other competitions or host your own that aims to solve problems in data-oriented research.
If you want any machine learning model to train and self learn, then one thing you must provide it with a lot of is data.
Now, that sounds easy until you come to realize that this data is very hard to come by.
So where do we get them?
16. Kaggle. It is one of the most popular sources of data for data science and machine learning engineers, and funny enough the data is free. With access to over 50,000 public datasets, let’s just say this platform provides you with all the code and data you need to get your data science work done.
17. Google Data Search. If Kaggle does not suffice, then your other great place to hit for huge datasets is Google data search. It works more like the usual search engine, except you get datasets returned. And when a dataset is returned, it also comes with information on the data format and when it was last updated.
18. Open Data on AWS. Even though I have included it here, you’ll less often look for your ML data here because their data collection is still pretty low. That being said, the datasets you’ll find on AWS are unique and differential themselves by focusing on satellite imagery, rice genomes and brain scans from scientific research.
I didn’t want to finish this list of top data science resources without mentioning blogs.
In my opinion, blogs are the best ways to keep yourself up to date with the latest developments in data science, artificial intelligence and machine learning, because blog content is pretty easy to consume.
They are also pretty easy to keep updated than say YouTube videos or Podcasts.
So here are three blogs that are all in on artificial intelligence and its applications in various sectors of the economy. The companies behind these blogs, like Nvidia, are also actively involved in AI research and often develop customs tools for AI engineers.
19. OpenAI blog. OpenAI is a technology company focused on paying it forward by providing a path to safe artificial intelligence through research and discovery. They recently developed an AI API that can be used in language processing applications. Through their blogs, you’ll get to learn about the latest advancements in AI, ML and data science in general.
20. Nvidia blog. I guess everyone already knows Nvidia for their super-powerful graphics cards used in laptops, desktops as well as gaming machines. They didn’t stop there though. Today Nvidia is actively involved in AI & ML research, and have even integrated these technologies into their products. Learn more about deep learning and big data from their blog.
21. Distill.pub. Contrary to the two other platforms I have mentioned above, Distill.pub is less of an IT company but more of an online journal facilitating the publication of different materials about machine learning. It encourages both traditional and non-traditional research in machine learning and its impact on day to day life.
In this day and age, there is no shortage of learning materials for almost any skill that you’d like to learn online.
So what’s the problem with that?
While this is supposed to be a good thing, it has created another problem called information overload. And most people getting into data science and machine learning often drop out because of information overload.
Because of the vast nature of these fields, it is not uncommon to get bogged down by the ton of learning materials and content that you are expected to go through.
So what’s the way out?
Pick one learning material and stick to it till you finish.
It is always easier to find the best resources for taking you to the next level once you skip the ‘beginner to artificial intelligence’ face.
I hope this list of the best AI and ML resources have provided you with clear options of the materials you need to use to start learning ML and AI and launch your career today.
Are there some other materials that are best for learning AI that I did not include in this list?
Please share your thoughts in the comments below.