Assigments for Coursera Machine Learning Course by Andrew Ng
- Warm-up Exercise
- Computing Cost (for One Variable)
- Gradient Descent (for One Variable)
- Feature Normalization
- Computing Cost (for Multiple Variables)
- Gradient Descent (for Multiple Variables)
- Normal Equations
- Sigmoid Function
- Logistic Regression Cost
- Logistic Regression Gradient
- Predict
- Regularized Logistic Regression Cost
- Regularized Logistic Regression Gradien
- Regularized Logistic Regression
- One-vs-All Classifier Training
- One-vs-All Classifier Prediction
- Neural Network Prediction Function
- Feedforward and Cost Function
- Regularized Cost Function
- Sigmoid Gradient
- Neural Network Gradient (Backpropagation)
- Regularized Gradient
- Regularized Linear Regression Cost Function
- Regularized Linear Regression Gradient
- Learning Curve
- Polynomial Feature Mapping
- Validation Curve
- Gaussian Kernel
- Parameters (C, sigma) for Dataset 3
- Email Preprocessing
- Email Feature Extraction
- Find Closest Centroids (k-Means)
- Compute Centroid Means (k-Means)
- PCA
- Project Data (PCA)
- Recover Data (PCA)
- Estimate Gaussian Parameters
- Select Threshold
- Collaborative Filtering Cost
- Collaborative Filtering Gradient
- Regularized Cost
- Regularized Gradient