Machine Learning and Data Science Using Python
Machine Learning and Data Science Using Python
Duration: 1h 56m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 866 MB
Genre: eLearning | Language: English
Duration: 1h 56m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 866 MB
Genre: eLearning | Language: English
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
More Info