Machine Learning

Machine Learning Exercises done in the online course of Stanford University.

• Exercise 1 - Linear Regression
  • Implement linear regression and get to see it work on data.
• Exercise 2 - Logistic Regression
  • Implement logistic regression and apply it to two different datasets.
• Exercise 3 - Multi-class Classification and Neural Networks
  • Implement one-vs-all logistic regression and neural networks to recognize hand-written digits.
• Exercise 4 - Neural Network Learning
  • Implement the backpropagation algorithm for neural networks and apply it to the task of hand-written digit recognition.
• Exercise 5 - Regularized Linear Regression and Bias/Variance
  • Implement regularized linear regression and use it to study models with different bias-variance properties.
• Exercise 6 - Support Vector Machines
  • Use support vector machines (SVMs) to build a spam classifier.
• Exercise 7 - K-Means Clustering and PCA
  • Implement the K-means clustering algorithm and apply it to compress an image.
• Exercise 8 - Anomaly Detection and Recommender Systems
  • Implement the anomaly detection algorithm and apply it to detect failing servers on a network.
  • Use collaborative filtering to build a recommender system for movies.