The course has not been tailored for machine learning, and many of the examples are about 2D and 3D graphic systems, which are much easier to visualize than the multidimensional spaces of machine learning problems. Here, you’ll find everything you need about vector spaces, linear transformations, matrix transformations, and coordinate systems. I recommend the linear algebra course in particular. And it’s free, which makes it even better.Īlthough each of the videos (which are also available on YouTube) explain a separate topic, going through the courses end-to-end provides a much richer experience. ![]() Sal Khan has done a great job of putting together a comprehensive collection of videos that explain different math topics. But my personal favorite is Khan Academy’s math courses. There are plenty of good textbooks, online courses, and blogs that explore these topics. You also need to know a good bit of statistics and probability, as well as differential and integral calculus, especially if you want to become more involved in deep learning. Extensive experience with linear algebra is a must-have-machine learning algorithms squeeze every last bit out of vector spaces and matrix mathematics. I would argue that you need a lot more than that. Many machine learning books tell you that having a working knowledge of linear algebra. Khan Academy’s online courses are an excellent resource to acquire math skills for machine learning And while I don’t expect you to have fun with machine learning math, I will also try my best to give you some guidelines on how to make the journey a bit more pleasant. In this post, I will introduce some of my favorite machine learning math resources. At some point in your exploration and mastering of artificial intelligence, you’ll need to come to terms with the lengthy and complicated equations that adorn AI whitepapers and machine learning textbooks. There are plenty of programming libraries, code snippets, and pretrained models that can get help you integrate machine learning into your applications without having a deep knowledge of the underlying math functions.īut there’s no escaping the mathematical foundations of machine learning. ![]() Both are correct, depending on what you want to achieve. ![]() How much math knowledge do you need for machine learning and deep learning? Some people say not much.
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