Overview Machine Learning

Writing Your First Python Program and Understanding Syntax

Python Overview and Applications

Adding Comments and Writing Clean Code

Assigning Multiple Values to Variables

Understanding Variables and Assignments

Overview of Python Data Types

Working with Numbers in Python

Strings Slicing, Modifying, and Concatenating

String Formatting, Escape Characters, and String Methods

Python Booleans and Logical Values

Python Operators

Sets Unique and Unordered Data Collections

Introduction to Python Lists

Working with Tuples in Python

Managing Dictionaries in Python

For Loops for Iteration

While Loops in Python

If, Else, and Elif Conditional Logic

Working with Lambda Functions

Introduction to Python Functions

Python Arrays Basics and Usage

Understanding Inheritance in Python

Iterators for Managing Data

Classes and Objects in Python

Python Scope Local and Global Variables

Python Polymorphism Reusing Code

Managing Dates and Times in Python

Working with Modules in Python

Performing Mathematical Operations with Python Math

Simplifying Tasks with Regular Expressions (RegEx)

Managing Packages with PIP

Using JSON for Data Management

Handling Errors with Try and Except

Taking User Input in Python

String Formatting Techniques

File Handling in Python Read, Write, and Delete

Overview

Machine Learning Pipeline

Tools and Libraries for Machine Learning in Python

Types of Machine Learning

Key Concepts Features, Labels, Training, and Testing

What is Machine Learning

Exploratory Data Analysis

Data Transformation and Encoding

Data Cleaning. Removing duplicates and fixing inconsistencies

Data Cleaning. Handling missing data

Feature Scaling Standardization and Normalization

Practical Implementation

Splitting Data TrainTest Split

Introduction to Linear Regression

Implementing Linear Regression in Python

Polynomial Regression

Practical Regression Project Predicting Housing Prices

Ridge, Lasso, and Elastic Net Regression

Understanding Logistic Regression

Decision Trees Basics and Implementation

k-Nearest Neighbors (k-NN) for Classification

Implementing Logistic Regression in Python

Support Vector Machines (SVM) Concepts and Implementation

Classification Project Heart Disease Prediction

Introduction to Ensemble Learning

Random Forest

Gradient Boosting Algorithms

Project - Credit card fraud detection using ensemble methods

K-Means Clustering

Hierarchical Clustering

Density-Based Clustering

Project - Customer Segmentation Using Clustering Algorithms

Principal Component Analysis (PCA)

Project - Visualizing Wine Data Using PCA and t-SNE

t-SNE (t-Distributed Stochastic Neighbor Embedding)

Autoencoders

Introduction to Association Rules - Market Basket Analysis

Apriori Algorithm

Project - Market Basket Analysis for E-commerce Data

FP-Growth Algorithm