Introduction
Why Choose Python for Data Science & Machine Learning
Prerequisites & Reminders
Python Quick Review
Overview & Objectives
A Quick Example
Getting & Processing Data
Data Visualization
Supervised & Unsupervised Learning
Regression
Simple Linear Regression
Multiple Linear Regression
Decision Tree
Random Forest
Classification
Logistic Regression
K-Nearest Neighbors
Decision Tree Classification
Random Forest Classification
Clustering
Goals & Uses of Clustering
K-Means Clustering
Anomaly Detection
Association Rule Learning
Explanation
Apriori
Reinforcement Learning
What is Reinforcement Learning
Comparison with Supervised & Unsupervised Learning
Applying Reinforcement Learning
Neural Networks
An Idea of How the Brain Works
Potential & Constraints
Here’s an Example
Natural Language Processing
Analyzing Words & Sentiments
Using NLTK
Model Selection & Improving Performance
Sources & References