Machine Learning and Data Science Using Python – Part 1

Module–1

Welcome to the Pre–Program Preparatory Content

Session–1:

1) Introduction

2) Preparatory Content Learning Experience

MODULE–2

INTRODUCTION TO PYTHON

Session–1:

Understanding Digital Disruption Course structure

1) Introduction

2) Understanding Primary Actions

3) Understanding es & Important Pointers

Session–2:

Introduction to python

1) Getting Started Installation

2) Introduction to Jupyter Notebook

The Basics Data Structures in Python

3) Lists

4) Tuples

5) Dictionaries

6) Sets

Session–3:

Control Structures and Functions

1) Introduction

2) If–Elif–Else

3) Loops

4) Comprehensions

5) Functions

6) Map, Filter, and Reduce

7) Summary

Session–4:

Practice Questions

1) Practice Questions I

2) Practice Questions II

Module–3

Python for Data Science

Session–1:

Introduction to NumPy

1) Introduction

2) NumPy Basics

3) Creating NumPy Arrays

4) Structure and Content of Arrays

5) Subset, Slice, Index and Iterate through Arrays

6) Multidimensional Arrays

7) Computation Times in NumPy and Standard Python Lists

8) Summary

Session–2:

Operations on NumPy Arrays

1) Introduction

2) Basic Operations

3) Operations on Arrays

4) Basic Linear Algebra Operations

5) Summary

Session–3:

Introduction to Pandas

1) Introduction

2) Pandas Basics

3) Indexing and Selecting Data

4) Merge and Append

5) Grouping and Summarizing Data frames

6) Lambda function & Pivot tables

7) Summary

Session–4:

Getting and Cleaning Data