*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

http://www.datasciencecentral.com/xn/detail/6448529:BlogPost:791852