hakanmhmd / machine-learning

Machine Learning by Andrew Ng

Stanford university ML course

Topics

1. Linear regression, Cost function, Gradient Descent, Normal equation
2. Logistic regression, Classification, Multiclass classification(one vs all)
3. Neural networks, Hidden layers
4. Forward propagation, Backpropagation, Gradient Checking
5. Evaluating a learning algorithm, Cross validation, Model selection, LR with Bias/Variance
6. Support Vector Machines, Kernels
7. K-Means Clustering (Unsupervised learning), Dimensionality Reduction with Principal Component Analysis
8. Anomaly Detection, Gaussian distribution, Content based recommendations, Collaborative filtering