The origins of machine learning

Uses and abuses of machine learning

Steps to apply machine learning to your data

Choosing a machine learning algorithm

Managing and Understanding Data

Exploring and understanding data

Lazy Learning – Classification Using Nearest Neighbors

Understanding classification using nearest neighbors

Diagnosing breast cancer with the kNN algorithm

Probabilistic Learning – Classification Using Naive Bayes

Example – filtering mobile phone spam with the naive Bayes algorithm

Divide and Conquer – Classification Using Decision Trees and Rules

Example – identifying risky bank loans using C5.0 decision trees

Understanding classification rules

Example – identifying poisonous mushrooms with rule learners

Forecasting Numeric Data – Regression Methods

Example – predicting medical expenses using linear regression

Understanding regression trees and model trees

Example – estimating the quality of wines with regression trees and model trees

Black Box Methods – Neural Networks and Support Vector Machines

Modeling the strength of concrete with ANNs

Understanding Support Vector Machines

Finding Patterns – Market Basket Analysis Using Association Rules

Understanding association rules

Example – identifying frequently purchased groceries with association rules

Finding Groups of Data – Clustering with k-means

Measuring performance for classification

Tuning stock models for better performance

Improving model performance with meta-learning

Specialized Machine Learning Topics