Python tutorials for introduction to machine learning

Introduction to Machine Learning in Python

This repository provides instructional material for machine learning in python. The material is used for two classes taught at NYU Tandon by Sundeep Rangan:

• EE-UY / CS-UY 4563: Introduction to Machine Learning (Undergraduate)
• EL-GY 6123: Introduction to Machine Learning (Graduate)

Anyone is free to use and copy this material (at their own risk!). But, please cite the material if you use the material in your own class.

Pre-requisites

• All the software can be run on any laptop (Windows, MAC or UNIX). Instructions are also provided to run the code in Google Cloud Platform on a virtual machine (VM).
• Both classes assume no python or ML experience. However, experience with some programming language (preferably object-oriented) is required.
• To follow all the mathematical details and to complete the homework exercises, the class assumes undergraduate probability, linear algebra and multi-variable calculus.

Start the course

Go to the units sequence to being the machine learning course.

Feedback

Any feedback is welcome. If you find errors, have ideas for improvements, or want to voice any other thoughts, create an issue and we will try to get to it. Even better, fork the repository, make the changes yourself and create a pull request and we will try to merge it in. See the excellent instructions from the former TA Ish Jain.

Contributors

The course material has been developed by several faculty including:

• Sundeep Rangan, ECE, NYU (Main developer)
• Yao Wang, ECE, NYU
• Alyson K. Fletcher, Statistics, ECE, UCLA