Basic-ML-Models (Tutorial flavor)

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).

Machine Learning Models context:

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)

Data Mining Models context:

14-Frequent Pattern Mining (FP-growth+Apriori)