ML-learning-path
Self learning guide for machine learning
A Learning Path for Machine Learning (ML)
A guided self learning path for Machine Learning
License
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright and Ownership
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.
Learning Path
Intro
Books
Provided as a reference. We will reference specific chapters throughout the guide.
- Recommended books - lot of free books
Meta Links
Useful links for ML.
Data
- Get data to practice ML
Part 1 - Prerequisites for ML
Python Basics
- 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
Python Data Analysis
- NUMPY-1 - Numpy Introduction
- PANDAS-1 - Pandas intro
- VIZ-1 - Visualizations intro
- STATS-1 - Basic stats
- DATA-1 - Data cleansing
Part 2 - Essential Machine Learning
Feature Engineering
- FE-1 - Feature engineering - intro
- FE-2 - Feature engineering - variable encoding
- FE-3 - Feature engineering - scaling data
Intro to Machine Learning
- ML-1: Understanding Machine Learning
- ML-2: Doing Machine Learning
Algorithms Overview
- A Tour of Machine Learning Algorithms
- ML Algorithm cheat sheet
- 101 Machine Learning Algorithms for Data Science with Cheat Sheets
- Scikit cheat sheet
SciKit-Learn Library
- SCIKIT-1 - Introduction to Scikit-Learn
Supervised Algorithms
Regression
Linear Regression
Classification
Logistic Regression
Support Vector Machines (SVM)
Naive Bayes (NB)
Model Validation
Tree Algorithms
Trees can do both regression and classification tasks