Machine Learning - Coursera.org
IV. Linear Regression with Multiple Variables (Week 2)
Linear Regression
- Warm up exercise
- Compute cost for one variable
- Gradient descent for one variable
- Feature normalization (optional)
- Compute Cost for Multiple Variables (optional)
- Gradient Descent for Multiple Variables (optional)
- Normal Equations (optional)
VII. Regularization (Week 3)
Logistic Regression
- Sigmoid Function
- Compute cost for logistic regression
- Gradient for logistic regression
- Predict Function
- Compute cost for regularized LR
- Gradient for regularized LR
VIII. Neural Networks: Representation (Week 4)
Multi-class Classification and Neural Networks
- Regularied Logistic Regression
- One-vs-all classifier training
- One-vs-all classifier prediction
- Neural Network Prediction Function
IX. Neural Networks: Learning (Week 5)
Neural Network Learning
- Feedforward and Cost Function
- Regularized Cost Function
- Sigmoid gradient
- Neural Net Gradient Function (Backpropagation)
- Regularized Gradient
X. Advice for Applying Machine Learning (Week 6)
Regularized Linear Regression and Bias/Variance
- Regularized Linear Regression Cost Function
- Regularized Linear Regression Gradient
- Learning Curve
- Polynomial Feature Mapping
- Cross Validation Curve
XII. Support Vector Machines (Week 7)
Support Vector Machines
- Gaussian Kernel
- Parameters (C, sigma) for Dataset 3
- Email Preprocessing
- Email Feature Extraction
XIV. Dimensionality Reduction (Week 8)
K-Means Clustering and PCA
- Find Closest Centroids
- Compute Centroid Means
- PCA
- Project Data
- Recover Data
XVI. Recommender Systems (Week 9)
Anomaly Detection and Recommender Systems
- Estimate Gaussian Parameters
- Select Threshold
- PCA
- Collaborative Filtering Cost
- Collaborative Filtering Gradient
- Regularized Cost
- Gradient with regularization