SUMMARY.md

Summary

Intro

• About this guide
• Top down learning
• Credits

Resources

• Recommended books
• Data

Python Basics

• Why Python
• Getting started with Python
• Learn Jupyter Notebook
• Bacis of Python - Work with numbers, strings and operators
• Control Statements - if/else conditions and loops
• 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

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

• Understanding Regressions

Linear Regression

• LR-1 Linear Regression intro

Classification

• Introduction to classifications

Logistic Regression

• Logistic Regression

Support Vector Machines (SVM)

• SVM

Naive Bayes (NB)

• Naive Bayes

Model Validation

• Model selection using Cross Validation

Tree Algorithms

Trees can do both regression and classification tasks

• Decision Trees
• Random Forest
• Boost Trees

Unsupervised Algorithms

• Unsupervised learning overview

Clustering

• KMeans

Dimension Reduction

• PCA