Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence.
When beginning your educational path, it's important to first understand how to learn ML. We've broken the learning process into four areas of knowledge, with each area providing a foundational piece of the ML puzzle. To help you on your path, we've identified books, videos, and online courses that will uplevel your abilities, and prepare you to use ML for your projects. Start with our guided curriculums designed to increase your knowledge, or choose your own path by exploring our resource library.
Coding skills: Building ML models involves much more than just knowing ML concepts—it requires coding in order to do the data management, parameter tuning, and parsing results needed to test and optimize your model.
Math and stats: ML is a math-heavy discipline, so if you plan to modify ML models or build new ones from scratch, familiarity with the underlying math concepts is crucial to the process.
ML theory: Knowing the basics of ML theory will give you a foundation to build on, and help you troubleshoot when something goes wrong.
Build your own projects: Getting hands-on experience with ML is the best way to put your knowledge to the test, so don't be afraid to dive in early with a simple collab or tutorial to get some practice.
Below are the top 7 Machine Learning courses for 2022.
1. Machine Learning — Coursera
This is the course for which all other machine learning courses are judged. This beginner's course is taught and created by Andrew Ng, a Stanford professor, co-founder of Google Brain, co-founder of Coursera, and the VP that grew Baidu’s AI team to thousands of scientists.
The course uses the open-source programming language Octave instead of Python or R for the assignments. This might be a deal-breaker for some, but Octave is a simple way to learn the fundamentals of ML if you're a complete beginner.
2. Deep Learning Specialization — Coursera
Also taught by Andrew Ng, this specialization is a more advanced course series for anyone interested in learning about neural networks and Deep Learning, and how they solve many problems.
The assignments and lectures in each course utilize the Python programming language and use the TensorFlow library for neural networks. This is naturally an excellent follow-up to Ng’s Machine Learning course since you’ll receive a similar lecture style but now will be exposed to using Python for machine learning.
3. Machine Learning Crash Course — Google AI
This course comes from Google AI Education, a completely free platform that's a mix of articles, videos, and interactive content.
The Machine Learning Crash Course covers the topics needed to solve ML problems as soon as possible. Like the previous course, Python is the programming language of choice, and TensorFlow is introduced. Each main section of the curriculum contains an interactive Jupyter notebook hosted on Google Colab. Video lectures and articles are succinct and straightforward, so you'll be able to quickly move through the course at your own pace.
#4 Machine Learning with Python — Coursera
Another beginner course, but this one focuses solely on the most fundamental machine learning algorithms. The instructor, slide animations, and explanation of the algorithms combine very nicely to give you an intuitive feel for the basics.
This course uses Python and is somewhat lighter on the mathematics behind the algorithms. With each module, you’ll get a chance to spool up an interactive Jupyter notebook in your browser to work through the new concepts you just learned. Each notebook reinforces your knowledge and gives you concrete instructions for using an algorithm on real data.
#5 Advanced Machine Learning Specialization — Coursera
This is another advanced series of courses that casts a very wide net. If you are interested in covering as many machine learning techniques as possible, this Specialization is the key to a balanced and extensive online curriculum.
The instruction in this course is fantastic: extremely well-presented and concise. Due to its advanced nature, you will need more math than any other courses listed so far. If you have already taken a beginner course and brushed up on linear algebra and calculus, this is a good choice to fill out the rest of your machine learning expertise.
#6 Machine Learning — EdX
This is an advanced course with the highest math prerequisite out of any other course on this list. You’ll need a very firm grasp of Linear Algebra, Calculus, Probability, and programming. The course has interesting programming assignments in either Python or Octave, but the course doesn’t teach either language.
One of the biggest differences with this course is the coverage of the probabilistic approach to machine learning. If you’ve been interested in reading a textbook, like Machine Learning: A Probabilistic Perspective — which is one of the most recommended data science books in Master’s programs — then this course would be a fantastic complement.
7. Introduction to Machine Learning for Coders — Fast.ai
Fast.ai produced this excellent, free machine learning course for those that already have roughly a year of Python programming experience.
It's astounding how much time and effort the founders of Fast.ai have put into this course — and other courses on their site. The content is based on the University of San Diego's Data Science program, so you'll find that the lectures are done in a classroom with students, similar to the MIT OpenCourseWare style.
The course has many videos, some homework assignments, extensive notes, and a discussion board. Unfortunately, you won't find graded assignments and quizzes or certification upon completion, so Coursera/Edx would be a better route for you if you'd rather have those features.
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