This repo is a perfect place for most of the people who are willing to start implementing their knowledge about machine learning algorithms. After studying the statistical and mathematical concepts about machine learning algorithms, someone can hardly resist to start coding for them. This repository covers python implementation of several algorithms in supervised and unsupervised learning.

Before implementing any algorithm of classification or regression, it is essential to analyze the data available. Moreover, data can be converted into more useful information if appropriately processed before use. Data pre-processing template can be used as a preliminary step before implementing most of the algorithms mentioned below.

Classification:

• Naive Bayes Classifier
• Random Forest Classifier
• Decision Tree
• K-Nearest Neighbor
• Support Vector Machine

Regression:

• Simple Linear Regression
• Polynomial Regression
• Multiple Linear Regression
• Decision Tree
• Logistic Regression
• Random Forest Regression
• Support Vector Regression

Clustering:

• Hierarchical Clustering
• K-Means Algorithm

Reinforcement Learning:

• Thompson_Sampling
• UCB
• Apriori Algorithm

• Machine Learning basics: https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/
• Books on statistical learning: https://web.stanford.edu/~hastie/ElemStatLearn/
• Python with ML : https://pythonprogramming.net/machine-learning-tutorial-python-introduction/