#293 overall
Mathematics for Machine Learning
Coursera
2 recommenders · 2 threads · recommended in 1 year, 2018 · score 1.6
A Coursera specialization from Imperial College London covering the linear algebra, multivariate calculus, and related math used in machine learning, including vectors, matrices, derivatives, and PCA. It is meant to be taken alongside applied ML study.
Commenters point to it as a structured path through the math behind ML, suggesting it after learning the basics of matrices and calculus or after trying applied ML first.
Recommendations by year
20062026
Essence of calculus[0] by 3blue1brown for the basics and the second course in the Coursera Mathematics for Machine Learning would let you get started. You would rarely need calculus more advanced than that covered in the above, and if need be you will be in a position to look it up quickly. If you can sustain your interest in ML over a long period of time and are in no hurry, I would recommend going through all the math mentioned. If you are a top-down learner, the fast.ai course on ML and deep learning for coders will get you started head-first. All the best!
[0] - https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53...
You should consider taking this course series "Mathematics for Machine Learning" from coursera. https://www.coursera.org/specializations/mathematics-machine...
However, if you know the very basics of matrices (multiplication, transpose) and calculus (derivatives of basic functions, and partial derivates, and chain rule) I'd highly recommend first trying basic applied ML before diving deep into the math. It'll help you see where the math you're learning is actually used, as you learn them.
Try deeplearning.ai first, then try this "math for ML" course.
Take a look at the Coursera specialization: Mathematics for Machine Learning [1]. The specialization isn't free but you can certainly apply for financial aid.
[1]: https://www.coursera.org/specializations/mathematics-machine...