Recommendations for learning the Mathematics of Machine Learning.
I picked 2 resources for each of these topics:
• Probability and Statistics
• Linear Algebra
• Multivariate Calculus
The first link is simpler. The second offers a more in-depth perspective.
Let's start ↓
1. Seeing Theory
An interactive website seeing-theory.brown.edu that takes you through some of the most critical probabilities and statistics concepts.
These should be enough to get you started, and you will have fun while going through it!
2. Statistics 110: Probability
If you want a more advanced overview of Probabilities and Statistics, this course from Harvard University is an excellent introduction:
youtube.com/playlist?list=…
3. Essence of Linear Algebra
Who doesn’t love Grant Sanderson’s YouTube videos?
Go through this playlist: youtube.com/playlist?list=…
It's an excellent refresher on Linear Algebra.
4. Linear Algebra
This is MIT Course 18.06 taught by Professor Gilbert Strang.
ocw.mit.edu/courses/mathem…
One of the best linear algebra courses that you’ll ever find.
Professor Gilbert makes the subject ridiculously simple and engaging.
5. Essence of Calculus
This is Grant Sanderson’s excellent take on calculus:
youtube.com/playlist?list=…
A series of videos that are informative and make calculus feel like something you could have discovered yourself.
6. Multivariate Calculus
This is an introductory course to build your confidence and introduce you to the multivariate calculus required to build many machine learning techniques.
coursera.org/learn/multivar…
Finally, a bonus:
Mathematics for Machine Learning and Data Science Specialization is a new specialization from @DeepLearningAI_.
coursera.org/specialization…?
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