This repository contains the course materials and assignments done as part of the Machine learning course by Andrew Ng.

Week 1

• What is Machine learning?
• Supervised Learning
• Unsupervised Learning
• Linear Regression with One Variable
• Linear Algebra Review

Week 2

• Linear Regression with Multiple Variables
  • Multivariate Linear Regression - Gradient Descent
  • Multivariate Linear Regression - Normal equation
  • Octave tutorial

Week 3

• Logistic Regression
  • Classification and Representation
  • Logistic Regression Model
  • Multiclass Classification
• Regularization
  • Solving the Problem of Overfitting

Week 4

• Neural Networks: Representation
  • Motivations
  • Neural Networks
  • Applications

Week 5

• Neural Networks: Learning
  • Cost Function and Backpropagation
  • Backpropagation in Practice
  • Application of Neural Networks - Autonomous driving

Week 6

• Advice for applying machine learning
  • Evaluating a Learning Algorithm
  • Bias vs. Variance
• Machine learning system design
  • Building a Spam Classifier
  • Handling Skewed Data
  • Using Large Data Sets

Week 7

• Support Vector Machines
  • Large Margin Classification
  • Kernels
  • SVMs in Practice

Week 8

• Unsupervised Learning
  • Clustering
• Dimensionality Reduction
  • Principal Component Analysis
  • Applying PCA