Machine Learning and Data Science Using Python

Short Description

Begin your ML and DS Journey

Description

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​

1) Introduction

2) Reading Delimited and Relational Databases​

3) Reading Data from Websites​

4) Getting Data from APIs​

5) Reading Data from PDF Files​

6) Cleaning Datasets​

7) Summary

Session-5:​

Practice Questions​

1) NumPy Practice Questions​

2) Pandas Practice Questions​

3) Pandas Practice Questions Solution

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