Machine Learning - Stanford University.
Taught by Prof. Andrew Ng.
- Supervised/Unsupervised Learning
- Linear Regression with One Variable
- Gradient Descent for Linear Regression
- Linear Algebra Review
[Slides 4-5] [Lecture Notes] [Assignment]
- Multivariate Linear Regression
- Normal Equation
- Octave/Matlab basic tutorial
[Slides 6-7] [Lecture Notes] [Assignment]
- Classification
- Logistic Regression Model
- Multiclass Classification
- Overfitting
- Regularization
[Slides 8] [Lecture Notes] [Assignment]
- Model Representation
- Multiclass Classification
[Slides 9] [Lecture Notes] [Assignment]
- Foward Propagation
- Backward Propagation
- Gradient Checking
- Random Initalization
[Slides 10-11] [Lecture Notes] [Assignment]
- Evaluating a hypothesis
- Bias vs Variance
- Regularization
- Error Analysis
- Handling Skewed Data
- Spam Classifier
[Slides 12] [Lecture Notes] [Assignment]
- Large Margin Classification with SVM
- Kernels
[Slides 13-14] [Lecture Notes] [Assignment]
- Clustering: K-means Algorithms
- Dimensionality Reduction: Principal Component Analysis (PCA)
[Slides 15-16] [Lecture Notes] [Assignment]
- Density Estimation
- Anomaly Detection
- Recommender System
- Stochastic Gradient Descent
- Mini-batch Gradient Descent
- Online Learning
- Sliding Window
- Machine Learning pipeline