My own set of Python methods/classes/snippets written during my hot love affair with Machine Learning. #What does this do? This is just a set of python files that I wrote to get a better understanding of ML concepts. Caution: Not for the faint hearted. If you do not have a sound Mathematical/Statistical background, turn back now. File Names and their description: #1. web_traffic_plot.py This code demonstrates the capabilities of scipy and matplotlib. Also, displays the power of Linear Regression. This code utilizes a tsv file with data from a web site for hits received/hour for 743 hours. This example tries to apply concepts of regression and finds the best fit for the data. #2. iris_dataset_all_combinations.py This code uses the "sklearn.datasets" package to load the classic "Iris Classification Problem" dataset and creates all possible outcomes in which data can be represented using "PyPlot". This will help potential data-scientists choose the best way to present data to the ML algorithm. #3. iris_dataset_classification_algorithm.py This code actually builds a classification model based on Program 2. The classification algorithm is based on simple mathematical concepts and does NOT contain sci-fi ML code.