Basic machine learning model implementations in python (jupyter notebook) with detailed comments and running demos, majorly referencing the book Machine Learning: A Probabilistic Perspective (Kevin P. Murphy) & Data Mining Concepts and Techniques (Morgan kaufmann).
01-Gaussian Interpolation
02-Ridge Regression
03-Polynomial Regression
04-Logistic Regression
05-Cross Validation
06-Naive Bayesian (Discrete)
07-Naive Bayesian (Gaussian)
08-Deep Neural Network (Configurable)
09-EM Algorithm for Estimating Missing Entries
10-SVM
11-PCA
12-Decision Tree & Bagging & Random Forest & Ada-Boosting (Using sklearn package)
13-Clustering (K-means, Accelerated K-means, Soft K-means, Gaussian Mixture Model - Expectation Maximization)
14-Frequent Pattern Mining (FP-growth+Apriori)