This cheat sheet covers all of the coding, intuition and application aspects of the foundational machine learning algorithms. This works assumes that you know what the algorithms are and how they work, and it is intended to be a quick reference on how to use them using Python libraries.

This cheat sheet does not explain the mathematical grounding behind each ML algorithm.

The work is based on a mixture of different resources. Notably:

• Kirill Eremenko's and Hadelin de Ponteve's Machine Learning A-Z™: Hands-On Python & R In Data Science course on Udemy. The cheat sheet only covers examples in Python, but the course covers both Python and R.
  • Note that the cheat sheet is not intended to be a replacement for the course.
• Provost's and Fawcett's Data Science for Business Book , which is an excellent resource for understanding the real-world and non-trivial business applications of machine learning.
• The University of Melbourne's Postgraduate course on Statistical Machine Learning.

This is a WIP and it will take a while. This TOC contains the cheat sheets that have been finalized.

• Part 1 - Data Preprocessing
• Part 4 - Clustering
  • K-means Clustering
• Part 5 - Association Rule Learning
  • Apriori
  • Eclat