Learn Python Programming – Easy as Pie

COURSE CONTENTS

• What is coding? – It’s a lot like cooking!
  • Introduction
  • Coding is like Cooking
  • Variables are like containers
  • Anaconda and Pip

• Don’t Jump Through Hoops, Use Dictionaries, Lists and Loops
  • A List is a list
  • Fun with Lists!
  • Dictionaries and If-Else
  • Don’t Jump Through Hoops, Use Loops
  • Doing stuff with loops
  • Everything in life is a list – Strings as lists

• Our First Serious Program
  • Modules are cool for code-reuse
  • Our first serious program : Downloading a webpage
  • A few details – Conditionals
  • A few details – Exception Handling in Python

• Doing Stuff with Files
  • A File is like a barrel
  • Autogenerating Spreadsheets with Python
  • Autogenerating Spreadsheets – Download and Unzip
  • Autogenerating Spreadsheets – Parsing CSV files
  • Autogenerating Spreadsheets with XLSXwriter

• Functions are like Foodprocessors
  • Functions are like Foodprocessors
  • Argument Passing in Functions
  • Writing your first function
  • Recursion
  • Recursion in Action

• Databases – Data in rows and columns
  • How would you implement a Bank ATM?
  • Things you can do with Databases – I
  • Things you can do with Databases – II
  • Interfacing with Databases from Python
  • SQLite works right out of the box
  • Build a database of Stock Movements – I
  • Build a database of Stock Movements – II
  • Build a database of Stock Movements – III

• An Object Oriented State of Mind
  • Objects are like puppies!
  • A class is a type of variable
  • An Interface drives behaviour

• Natural Language Processing and Python
  • Natural Language Processing with NLTK
  • Natural Language Processing with NLTK – See it in action
  • Web Scraping with BeautifulSoup
  • A Serious NLP Application : Text Auto Summarization using Python
  • Autosummarize News Articles – I
  • Autosummarize News Articles – II
  • Autosummarize News Articles – III

• Machine Learning and Python
  • Machine Learning – Jump on the Bandwagon
  • Plunging In – Machine Learning Approaches to Spam Detection
  • Spam Detection with Machine Learning Continued
  • News Article Classification using K-Nearest Neighbors
  • News Article Classification using Naive Bayes
  • Code Along – Scraping News Websites
  • Code Along – Feature Extraction from News articles
  • Code Along – Classification with K-Nearest Neighbours
  • Code Along – Classification with Naive Bayes
  • Document Distance using TF-IDF
  • News Article Clustering with K-Means and TF-IDF
  • Code Along – Clustering with K-Means