Course XCS229i in Machine Learning from Stanford University
This course has 2 focuses: on Matemathical derivation of models and Python Implementation of the models. Then, it is applied on data.
The course is split into 5 parts:
- Part I: Feature Maps and Single Neural Networks
- Part II: Logistic Regression (GDA) and Poission Regression
- Part III: Constructing Kernels
- Part IV: Naive Bayes for Spam classification and ANN used for MNIST classfication
- Part V: Gaussian Mixture Models (GMM) and K-means
