Learning the very Basics of Python and moving on to very basic Machine Learning with Python

Machine-Learning-with-Python -

This repository will contain both code and additional reading material refrences for analytics and Machine-Learning-with-Python - Machine-Learning-with-Python

If you are reading this and can code very basic Python - you are Welcome to contribute.

Table of Contents of this repository

• A-- Intro to Python(Mostly Python-3)...
• B-- Download and Preprocess various kinds of Data
• C-- Further explorations EDA with Data
• D-- Visualizations for EDA
• Work in Progress
• Work in Progress
• Work in Progress

References - Always an ongoing effort - Work in Progress

Learning Index :-

Module -1 :-

Whats ML ? Why Python ? some bits of theory before the Code :)

Quick intro to - Numpy , Pandas , MatplotLib , Bokeh , Seaborn and SciKitLearn.

1. Introduction to Regression - Theory and Excel workbook examples

   Regression in Python - the very basics .

   Case Study -1 - Linear Regression within Python

   Choosing "the Best" Regression Model.

2. Classification Tasks - the k-Nearest Neighbour

   Related case study and examples .

Questions and Answers.

Module -2 :-

1. Decision Trees from Scratch - Source - Book - Programming Collective Intelligence.

Related case study and examples .

Questions and Answers.

Module -3 :-

1. Logistic Regression with Python. - from sklearn.linear_model import LogisticRegression

2. Gradient Boosting Classifier - Using sample code from Own - Titanic Dataset - Kaggle attempt.

3. Random Forest Classifer - Using sample code from Own - Titanic Dataset - Kaggle attempt.

Related case study and examples .

1. Using the Titanic - Kaggle Data Set -will build this Classification problem with using sample code from Own Kaggle attempt.

Questions and Answers.

Module -4 :-

1. k-Means clustering with Python.

Related case study and examples .

Questions and Answers.

Module -5 :-

1. Visualizing data with Python.

   Intermediate - MatPlotLib ,Bokeh Seaborn and JavaScript Lib - D3.js.

   Related case study and examples .

Hands on Exercises - Questions and Answers.

Module -6 :-

1. Intro to NLP [ Natural Language Processing ] - NLTK

   Web Scraping - Beautiful Soup - BS4 , stack

   Related case study and examples :-

   1. The internal workings of a Basic Chat Bot - ELISA Code and functionality.
   2. Creating your own Facebook Messenger (WebHook) ChatBot and hosting the same on Heroku - https://github.com/RohitDhankar/Heroku_Django_ChatBot_FacebookMessenger

Hands on Exercises - Questions and Answers.

Recap of earlier Modules and Important External Links :-

ANACONDA - Silent Install

ANACONDA - Official Page

ScikitLearn - Installation - After installing ANACONDA - pip install -U scikit-learn

Scikitlearn - Official Tutorials :-

Scikitlearn -Regression

Scikitlearn -Classification

Scikitlearn -Clustering

Scikitlearn -Model Selection etc

Numpy - Basics Official Tutorial

Installing -for LINUX and Mac - Numpy + Scipy Stack

Installing -for Windows - Numpy + Scipy Stack

Pandas - Basics Official Tutorial

MatPlotLib - PyPlot

Seaborn - Seaborn is a Python visualization library based on matplotlib

Installing Seaborn - Getting Started Official Guide