Mathematical Aspects of Machine Learning

View the Project on GitHub jeremy9959/Math-5800-Spring-2020

Learning Python for ML

This page is intended to collect tutorial resources that people can use to learn or improve their knowledge of Python.

The best way to learn any programming language is to try to use it. You’ll never be able to read a book and remember the syntax, but if you try to do a small project it will drive the ideas home.

OReilly library

We are fortunate at UConn to have access to the entire O’Reilly Library. From that home page:

From here you can get online access to every book in the massive O’Reilly library. Some useful specifics:

  1. Introducing Python, 2nd Edition. This is an elementary introduction to the language from the beginning. Good for programming novices, for whom Python is the first real computer language.

  2. Python for Programmers, 1st Edition. I don’t know this book, but it looks interesting: it’s intended for experienced programmers learning Python and claims to have examples from Machine Learning to illustrate its points.

  3. Python for Data Analysis, 2nd Edition. Much of this book is devoted to the pandas library, which is the python library for manipulating data. Proficiency with pandas makes it possible to do a lot of data manipulation quickly and efficiently.

There are other, more advanced books, including introductions to TensorFlow, all available free on this site.


There are a lot of online courses on the web that teach python. They are of varying quality and some are quite expensive. One option that is affordable and pretty comprehensive is this Python Bootcamp. It covers an enormous amount of stuff, with lots of examples, and you would probably only need the first 5 or 6 sections to really get going. If you use your uconn email, this should only cost you $9.99. DO NOT PAY MORE! If you get a higher price quoted, let me know.