Machine Learning and Data Science Using Python

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Machine Learning and Data Science Using Python

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 Data Science and Machine Learning

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