This is a fork from the brillian work done by (Kishan)[https://github.com/kishi001] and builds on most of the conepts that they have introduced.

Python is a general-purpose interpreted, interactive, object-oriented, and high-level programming language. It was created by Guido van Rossum during 1985- 1990.

Python is easy to pick as a programming language even if you're completely new to programming.

Mac OS X and Linux comes pre installed with python. Windows users can download python from https://www.python.org/downloads/ .

Please don't use the defualt python from Mac OSX and Linux. Download a fresh install. For beginers, Anaconda is the recommended easy path of installation.

Download Anaconda from https://www.anaconda.com/download/

Anaconda is completely free and includes more than 300 python packages. Both python 2.7 and 3.x options are available.

Windows Users. Please make sure to set environent variable after Anaconda installation

By Default Anaconda is installed on the following locatio in windows -

C:\Users<>\Anaconda3\

Following entries need to be set -

C:\Users<>\Anaconda3\Scripts\
C:\Users<>\Anaconda3\Library\
C:\Users<>\Anaconda3\Lib\site-packages\

To install IPython run, <>

$ pip install ipython[all] This will install all the necessary dependencies for the notebook, qtconsole, tests etc.

Installation from unofficial distributions

Installing all the necessary libraries might prove troublesome. Anaconda comes pre packaged with all the necessary python libraries and also IPython.

Notebooks are one of key piece of Python Ecosystem that enables us to quickly see feedback and iterate. More info - https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/what_is_jupyter.html

1 - Basic Python

1.1 - Variable and Operators

1.2 - Built-in functions

1.3 - Lists, Tuples and Sets

1.4 - Strings and Dictionaries

1.5 - Control Flow Statements

1.6 - Functions

1.7 - Classes

2 - Numpy

2.1 - Numpy array creation

2.2 - Datatypes of Numpy arrays

2.3 - Array Operations

2.4 - Array slicing and indexing

2.5 - File Operations

3 - Pandas

3.1 - Series

3.2 - DataFrames

3.3 - Missing Data

3.4 - Descriptive

3.5 - Groupby

4 - Matplotlib

4.1 Matplotlib Concepts

5 - Website Scraping

5.1 - Overview of HTML and CSS (For Discussion)

5.2 - Scraping using BeautifulSoup

5.3 - BeautifulSoup with Selenium

5.4 - Overview of Scrapy (For discussion)

6 - Integration with Databases

6.1 - Overview on integration with DBs

7 - Machine Learning

7.1 - Linear Regression

7.2 - Logistic Regression

7.3 - K-Nearest-Neighbors

7.4 - Decision Tree and Random Forests

7.5 - Support Vector Machines

7.6 - K Means Clustering

7.7 - Principal Component Analysis

7.8 - Recommender System

7.9 - Natural Language Processing

8 - Real life applications with Python

8.1 - Creating a Webmaps with Python and Folium (would demo)

8.2 - Building a Website Blocker

8.3 - Building a Website with Python and Flask

8.4 - Building GUI with Tkinter

8.5 - Building a Webcam Motion Detector (would demo)

8.6 - Building a Scraper to scrape data