The Math Required for Machine Learning

The Math Required for Machine Learning


This article was written by Harsh Sikka. This version is a summary of the original article.

Start with  Mathematics for Machine Learning Specialization on Coursera. If starting from complete scratch, the topics you should certainly review/cover, in any order are as follows:

Linear Algebra — Professor Strang’s textbook and MIT Open Courseware course are recommended for good reason. Khan Academy also has some great resources, and there is a helpful set of review notes from Stanford.
Multivariate Calculus — Again, MIT Open Courseware has good courses, and so does Khan Academy.
Probability — Stanford’s CS 229, a course I’ve mentioned later, has an awesome probability review worth checking out.

Once you’ve finished the resources above, I’d say you’re in a great place to tackle the Andrew Ng Coursera Course or its more mature, mathematically rigorous older brother, CS 229.

To read the original article, click here.
DSC Resources

Book and Resources for DSC Members
Comprehensive Repository of Data Science and ML Resources
Advanced Machine Learning with Basic Excel
Difference between ML, Data Science, AI, Deep Learning, and Statistics
Selected Business Analytics, Data Science and ML articles
Hire a Data Scientist | Search DSC | Find a Job
Post a Blog | Forum Questions

 
 

Link: The Math Required for Machine Learning