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-- Python-3 basics , data transforms etc.
PySpark
pandas
B-- Download and Preprocess various kinds of Data
data_aggregations
C-- Active Learning - mostly Lightly
testing Lightly
1-Lightly Active Learning -YouTube
2-Lightly Active Learning-SimSiam -YouTube
3-Lightly Active Learning- -YouTube
4-ActiveLearn-FeatureExtraction-Resnet50
D-- Visualizations for EDA
E-- detectron2 from Facebook AI Research- for Object Detection
detectron2 from FAIR
F-- Tornado - async framework
[Tornado] (https://github.com/RohitDhankar/Machine-Learning-with-Python_ML_Py/tree/master/dev_tornado)
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.

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.

Classification Tasks - the k-Nearest Neighbour

Related case study and examples .

Questions and Answers.

Module -2 :-

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

Related case study and examples .

Questions and Answers.

Module -3 :-

Logistic Regression with Python. - from sklearn.linear_model import LogisticRegression
Gradient Boosting Classifier - Using sample code from Own - Titanic Dataset - Kaggle attempt.
Random Forest Classifer - Using sample code from Own - Titanic Dataset - Kaggle attempt.

Related case study and examples .

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

Questions and Answers.

Module -4 :-

k-Means clustering with Python.

Related case study and examples .

Questions and Answers.

Module -5 :-

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 :-

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