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.