Resources by Type

Course's

A Course in Machine Learning

CIML is a set of introductory materials that covers most major aspects of modern machine learning (supervised learning, unsupervised learning, large margin methods, probabilistic modeling, learning theory, etc.).

Introduction to Machine Learning for Coders

Welcome to Introduction to Machine Learning for Coders! taught by Jeremy Howard (Kaggle's #1 competitor 2 years running, and founder of Enlitic). Learn the most important machine learning models, including how to create them yourself from scratch, as well as key skills in data preparation, model validation, and building data products.

Computer Programming using Python

This website teaches computer programming. This skill is very useful: with programming you can automate computer tasks, make art and music, interpret and analyze survey results, build tools for other people, create custom websites, write games, examine genetic data, connect people with each other, and the list goes on and on.

Stanford Deep Learning Tutorial

This tutorial will teach you the main ideas of Unsupervised Feature Learning and Deep Learning. By working through it, you will also get to implement several feature learning/deep learning algorithms, get to see them work for yourself, and learn how to apply/adapt these ideas to new problems.

The Landing Page Conversion Course

In this actionable course, landing page expert Oli Gardner will walk you through how to create high-converting landing pages. Follow along with step-by-step instructional videos to help you create your own landing pages as you go.

Algorithms by Jeff Erickson

This web page contains a free electronic version of my self-published textbook Algorithms, along with other lecture notes I have written for various theoretical computer science classes at the University of Illinois, Urbana-Champaign since 1998.