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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