guided-machine-learning

Self learning guide for machine learning

View the Project on GitHub elephantscale/guided-machine-learning

A Learning Path for Machine Learning (ML)

A guided self learning path for Machine Learning

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License

Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)

Creative Commons License
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.


Learning Path

Intro

Books

Provided as a reference. We will reference specific chapters throughout the guide.

Useful links for ML.

Data

Part 1 - Prerequisites for ML

Python Basics

Python Data Analysis

Part 2 - Essential Machine Learning

Feature Engineering

Intro to Machine Learning

Algorithms Overview

SciKit-Learn Library

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

Unsupervised Algorithms

Clustering

Dimension Reduction