This repo contains the content for IDMA part time program. The repo will remain a work in progress.

1. Pre-requisites

   • Install anaconda on your system. Visit here to download the software.

   • After installation you should be able to see anaconda prompt in your search bar

   • Revise basic ideas of programming such as variables, loops, conditionals.

2. Readings (Some resources require Orielly Credentials)

   Pre-reads

   • Python 101: Mark Lutz, Learning Python, 5th Edition, Chapter 4

   • Learning Objectives: Develop a basic familiarity with basic python datastructures, understand how to get help in a python interpretor.

   • Oops for DS:Mark Lutz, Learning Python, 5th Edition, Chapter 26

   • Learning Objectives: Understand the idea of classes, objects and inheritance.

   • Pandas 101:Matt Harisson, Pandas 1.x Cookbook, Chapter1

   • Learning Objectives: Develop familiarity with dataframe and series objects in pandas, develop comfort in working with chaining of operations.

   Post Session Reads

   • Python 101 Mark Lutz, Learning Python, 5th Edition, Chapter 10, 13, 14

   • Learning Objectives: Be able to write for loops using list comprehension and zip constructs. Be confident in using conditionals and loops in python.

   • Oops for DS: Mark Lutz, Learning Python, 5th Edition, Chapter 27. You can skim through the portion on operator overloading.

   • Learning Objectives: Be able to write a python class, impliment inheritance and use double dunder methods.

   • Pandas 101: Matt Harisson, Pandas 1.x Cookbook, Chapter2, Chapter3, Chapter4, Chapter5, Chapter6, Chapter7, Chapter9 You can complete these readings over a span of 2-3 weeks. As the course progresses you will find yourself using this book as a quick reference.

   • Learning Objectives: Be able to decide how to reduce memory footprint by changing datatypes of columns. Be aware of different data import/export mehods in pandas. Understand the need for index alignment and be able change indices to align them properly.

   ML Unit 2:

   • Lecture5: Regression
   • Lecture6: Non linear classification
   • Lecture7: Collaborative Filtering

   ML Unit 3:

   • Lecture8: Introduction to Neural Networks
   • Lecture9: Feed forward neural networks, backpropagation and stochastic gradient descent