A guided self learning path for Machine Learning
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
This guide is created by and copyrighted to Sujee Maniyam (2020)
The articles and videos referenced in this guide are owned, copyrighted by their respective owners.
Provided as a reference. We will reference specific chapters throughout the guide.
- Recommended books - lot of free books
Useful links for ML.
- Get data to practice ML
- Why Python
- PYTHON-1 - Getting started with Python
- PYTHON-2 - Learn Jupyter Notebook
- PYTHON-3 - Bacis of Python - Work with numbers, strings and operators
- PYTHON-4 - Control Statements - if/else conditions and loops
- PYTHON-5 - Data structures - Work with arrays, lists, sets and dictionaries
- Python resources
- NUMPY-1 - Numpy Introduction
- PANDAS-1 - Pandas intro
- VIZ-1 - Visualizations intro
- STATS-1 - Basic stats
- DATA-1 - Data cleansing
- FE-1 - Feature engineering - intro
- FE-2 - Feature engineering - variable encoding
- FE-3 - Feature engineering - scaling data
- ML-1: Understanding Machine Learning
- ML-2: Doing Machine Learning
- A Tour of Machine Learning Algorithms
- ML Algorithm cheat sheet
- 101 Machine Learning Algorithms for Data Science with Cheat Sheets
- Scikit cheat sheet
- SCIKIT-1 - Introduction to Scikit-Learn
Trees can do both regression and classification tasks