Machine Learning Exercises

Coursera Course : Machine Learning by Stanford University

• Exercise 1 - Linear Regression
  • Linear Regression Cost Function
  • Gradient Descent
  • Normal Equations
• Exercise 2 - Logistic Regression
  • Logistic Regression Cost Function
  • Logistic Regression Regularized Cost Function
• Exercise 3 - Multi-class Classification and Neural Networks
  • Multi-class Classification
  • Vectorizing Logistic Regression
  • One-vs-all Classification
  • Neural Network Prediction Function
• Exercise 4 - Neural Networks Learning
  • NN Feedforward and Cost Function
  • Sigmoid Gradient
  • Backpropagation
• Exercise 5 - Regularized Linear Regression and Bias vs Variance
  • Regularized Linear Regression
  • Polynomial Regression
• Exercise 6 - Support Vector Machines
  • SVM with Gaussian Kernels
  • Spam Email Classification
• Exercise 7 - K-means Clustering and Principal Component Analysis
  • K-means Clustering
  • Dimensionality Reduction with PCA
• Exercise 8 - Anomaly Detection and Recommender Systems
  • Anomaly detection
  • Collaborative filltering learning algorithm

Help Material :

what makes the gaussian kernel so magical for pca

The Gaussian Kernel

Taught by: Andrew Ng,