Machine Learning - Coursera University of Washington
- Week 1: Introduction & Simple Linear Regression
- Week 2: Multiple Regression
- Week 3: Assessing Performance
- Week 4: Ridge Regression
- Week 5: Feature Selection & Lasso
- Week 6: Nearest Neighbors & Kernel Regression
- Week 1: Linear Classifiers & Logistic Regression
- Week 2: Overfitting & Regularization in Logistic Regression
- Week 3: Decision Trees
- Week 4: Preventing Overfitting in Decision Trees & Handling Missing Data
- Week 5: Boosting (Adaboost)
- Week 6: Precision-Recall
- Week 7: Scaling to Huge Datasets & Online Learning
- Week 1: Introduction
- Week 2: Nearest Neighbor Search (Brute-Force, KD-tree and LSH)
- Week 3: Clustering with k-means
- Week 4: Mixture Models (GMM and EM algorithm)
- Week 5: Mixed Membership Modeling via Latent Dirichlet Allocation
- Week 6: Hierarchical Clustering