Table of Contents

• Preface

Week 1: Welcome

• 1.1 What is Machine Learning?
• 1.2 Linear Regression with One Variable

Week 2: Linear Regression with Multiple Variables

• 2.1 Multivariate Linear Regression
• 2.2 Computing Parameters Analytically
• 2.3 Octave/Matlab Tutorial

Week 3: Logistic Regression

• 3.1 Logistic Regression
• 3.2 Regularization

Week 4: Neural Networks: Representation

• 4.1 Neural Networks Representation

Week 5: Neural Networks: Learning

• 5.1 Neural Networks Learning
• 5.2 Backpropagation in Practice

Week 6: Advice for Applying Machine Learning

• 6.1 Advice for Applying Machine Learning
• 6.2 Machine Learning System Design

Week 7: Support Vector Machines

• 7.1 Support Vector Machines

Week 8: Unsupervised Learning

• 8.1 Unsupervised Learning
• 8.2 Dimensionality Reduction

Week 9: Anomaly Detection

• 9.1 Anomaly Detection
• 9.2 Recommender Systems

Week 10: Large Scale Machine Learning

• 10.1 Large Scale Machine Learning

Week 11: Application Example: Photo OCR

• 11.1 Application Example: Photo OCR

Appendix

GitHub Repo: Halfrost-Field

Follow: halfrost · GitHub

Source: https://github.com/halfrost/Halfrost-Field/blob/master/contents/Machine_Learning/contents.md