Machine Learning

Finished by March 2017, offered on Coursera by Professor Andrew Ng from Stanford University.

Below are the main topics of this course:

1. Supervised learning - labeled data

• Linear regression
• Logistic regression
• Neural networks
• Support vector machines

2. Unsupervised learning - unlabeled data

• K-means
• PCA
• Anomaly detection

3. Special applications/topics

• Recommender systems
• Large scale machine learning

4. Advice on building machine learning systems

• Bias and variance
• Regularization
• Deciding what to work on next when developing a system
• Evaluation of learning algorithms
• Learning curves
• Error analysis
• Ceiling analysis

This folder contains 8 exercises focusing on:

• 1 Linear Regression
• 2 Logistic Regression
• 3 Multi-class Classification and Neural Networks
• 4 Neural Networks Learning
• 5 Regularized Linear Regression and Bias v.s. Variance
• 6 Support Vector Machines
• 7 K-means Clustering and Principal Component Analysis
• 8 Anomaly Detection and Recommender Systems

Congratulation page upon finishing this course: