Machine Learning and Data Science Using Python (2021)

Begin your ML and DS Journey
What you'll learn:
Introduction to Python
Data Structures in Python
Control Structures and Functions
Python for Data Science
Introduction to NumPy
Operations on NumPy Arrays
Introduction to Pandas
Getting and Cleaning Data
Data Visualisation in Python
Introduction to Data Visualisation
Basics of Visualisation
Plotting Data Distributions
Plotting Categorical and Time-Series Data

Requirements
No programming experience is needed.

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

Module-4

Session-1:​

Vectors and Vector Spaces​

1) Introduction to Linear Algebra​

2) Vectors: The Basics​

3) Vector Operations - The Dot Product​

4) Dot Product - Example Application​

5) Vector Spaces​

6) Summary

Session-2:​

Linear Transformations and Matrices​

1) Matrices: The Basics​

2) Matrix Operations - I​

3) Matrix Operations - II

4) Linear Transformations​

5) Determinants​

6) System of Linear Equations​

7) Inverse, Rank, Column and Null Space​

8) Least Squares Approximation​

9) Summary

Session-3:​

Eigenvalues and Eigenvectors​

1) Eigenvectors: What Are They?​

2) Calculating Eigenvalues and Eigenvectors​

3) Eigen decomposition of a Matrix​

4) Summary

Session-4:​

Multivariable Calculus

Module-5

Session-1:​

Introduction to Data Visualisation​

1) Introduction: Data Visualisation​

2) Visualisations - Some Examples​

3) Visualisations - The World of Imagery​

4) Understanding Basic Chart Types I​

5) Understanding Basic Chart Types II​

6) Summary: Data Visualisation

Session-2:​

Basics of Visualisation Introduction​

1) Data Visualisation Toolkit​

2) Components of a Plot​

3) Sub-Plots​

4) Functionalities of Plots​

5) Summary

Session-3:​

Plotting Data Distributions Introduction​

1) Univariate Distributions​

2) Univariate Distributions - Rug Plots​

3) Bivariate Distributions​

4) Bivariate Distributions - Plotting Pairwise Relationships​

5) Summary

Session-4:​

Plotting Categorical and Time-Series Data​

1) Introduction​

2) Plotting Distributions Across Categories​

3) Plotting Aggregate Values Across Categories​

4) Time Series Data​

5) Summary

Session-5:​

1) Practice Questions I​

2) Practice Questions II

Who this course is for
Beginner Python developers curious about Machine Learning


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