directory

  • preface

Week 1: Welcome

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

Linear Regression with Multiple Variables

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

Week 3: Logistic Regression

  • 3.1 Logistic Regression
  • 3.2 Regularization

-sheldon: Neural Networks of 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 those

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

Making Repo: Halfrost – Field

Follow: halfrost dead simple

Source: github.com/halfrost/Ha…