Many readers of my book, Python Machine Learning, contacted me for advice on resources to brush up on math. Since many people do not have the time or motivation to spend years to work through traditional mathematics textbooks or courses, I thought it may be worthwhile to put some resources out there that bring machine learning practicioners up to speed with the absolute basics.

Hence, back in 2015, I started writing on a book “Mathematics for Machine Learning,” which was briefly shared on LeanPub. However, while writing this book, I soon realized that I would likely be unable to finish it in forseeable future due to a shift in priorities. However, before hiding away the contents that I had already written or drafted, I thought it might be useful to share it nonetheless in some form.

Below is a list of resources that I have written that may or may not be useful to introduce you to certain mathematical topics.



  • Appendix A: Mathematical Notation [PDF]

  • Appendix B: Algebra Basics [PDF]

  • Appendix C: Linear Algebra Essentials

  • Appendix D: Calculus and Differentiation Primer [PDF]

  • Appendix E: Probability Theory Overview



Other useful resources