Thread by @svpino

If you are looking to get a background in math before starting with machine learning, here is all the material you need covering the following topics:

• Probabilities & Statistics
• Linear Algebra
• Multivariate Calculus

More than enough to get started.

↓ 1/7

Seeing Theory

seeing-theory.brown.edu

An interactive website that will take you through some of the most critical concepts of Probabilities and Statistics.

These will be enough to get you started, and you will have fun while going through it!

↓ 2/7

Statistics 110: Probability

youtube.com/playlist?list=…

If you are looking for more, this course from Harvard University is an excellent introduction to probability as a language and a set of tools for understanding statistics, science, risk, and randomness.

↓ 3/7

Essence of Linear Algebra

youtube.com/playlist?list=…

Who doesn't love Grant Sanderson's YouTube videos?

Go through this playlist for a refresher in Linear Algebra, and you'll be more than ready to face any machine learning demons.

↓ 4/7

Linear algebra

ocw.mit.edu/courses/mathem…

MIT Course 18.06.

Taught by Profesor Gilbert Strang, this is simply one of the best linear algebra courses that you'll ever find.

Prof. Gilbert makes the subject ridiculously simple and engaging.

↓ 5/7

Mathematics for Machine Learning: Multivariate Calculus

coursera.org/learn/multivar…

A free, beginner-friendly introductory course to building your confidence and introduce you to the multivariate calculus required to build many common machine learning techniques.

↓ 6/7

Essence of Calculus

youtube.com/playlist?list=…

This is Grant Sanderson's excellent take on calculus.

A series of interactive videos that are informative and make calculus feel like something that you could have discovered yourself.

↓ 7/7

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