machine-learning-cheatsheet

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machine-learning-cheatsheet

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A cheat sheet that covers all of the foundational concepts of machine learning in Python.

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# Overview

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](https://www.udemy.com/share/101Wci/) 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](https://www.amazon.com.au/dp/1449361323/ref=cm_sw_em_r_mt_dp_U_6V8rEbN00QKMA)
, 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](https://handbook.unimelb.edu.au/2020/subjects/comp90051).

# Table of Content

This is a WIP and it will take a while. This TOC contains the cheat sheets that have been finalized.
- [Part 1 - Data Preprocessing](cheatsheets/Part%201%20-%20Data%20Preprocessing/data-preprocessing.md)
- Part 4 - Clustering
  - [K-means Clustering](cheatsheets/Part%204%20-%20Clustering/k_means_clustering.md)
- Part 5 - Association Rule Learning
  - [Apriori](cheatsheets/Part%205%20-%20Association%20Rule%20Learning/apriori.md)
  - [Eclat](cheatsheets/Part%205%20-%20Association%20Rule%20Learning/eclat.md)

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