Artificial Intelligence & Machine Learning (AI/ML)

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Last Update March 19, 2026

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About This Course

Artificial Intelligence and Machine Learning (AI/ML) are transforming industries by enabling computers to learn from data and make intelligent decisions. This course provides a comprehensive, hands-on introduction to AI and Machine Learning using Python, covering core concepts such as supervised learning, unsupervised learning, clustering, recommendation systems, and real-world model development.

You will learn how to work with data, train machine learning models, and apply AI techniques to solve practical business problems. Through real-world examples, projects, and practical exercises, students will gain industry-ready skills required for roles like Machine Learning Engineer, Data Analyst, and AI Developer.

Whether you are a beginner, student, or working professional, this course will help you build strong AI/ML foundations and practical experience using modern tools and frameworks widely used in the industry.

By the end of this course, you will be able to build, evaluate, and deploy machine learning models confidently.

Learning Objectives

Understand core concepts of Artificial Intelligence and Machine Learning

Use Python and industry-standard libraries like NumPy, Pandas, and Scikit-Learn

Build supervised and unsupervised machine learning models

Work with real-world datasets, preprocessing, training, and evaluation

Develop recommendation systems and intelligent AI solutions

Create real-world machine learning projects for your portfolio

Material Includes

Video lessons with practical demonstrations
Hands-on projects and practice exercises
Downloadable code, datasets, and learning resources
Quizzes and course completion certificate

Target Audience

Beginners and students interested in AI, ML, and Data Science
Job seekers preparing for AI, ML, or Data Analyst roles
Developers and IT professionals upgrading to AI/ML skills
Career switchers entering high-demand technology fields

Curriculum

57 Lessons

Module 1: Introduction to Artificial Intelligence & Machine Learning

Introduction to Artificial Intelligence

Introduction to Machine Learning

AI vs Machine Learning vs Deep Learning

Relationship between AI and Machine Learning

Data Science and Machine Learning

Module 2: Machine Learning Concepts

Module 3: Supervised Learning

Module 4: Unsupervised Learning

Module 5: Recommendation Systems

Module 6: Introduction to Python

Module 7: Python Programming for Machine Learning

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Artificial Intelligence & Machine Learning (AI/ML)

About This Course

Tags

Learning Objectives

Material Includes

Target Audience

Curriculum

Module 1: Introduction to Artificial Intelligence & Machine Learning

Introduction to Artificial Intelligence

Introduction to Machine Learning

AI vs Machine Learning vs Deep Learning

Relationship between AI and Machine Learning

Data Science and Machine Learning

Module 2: Machine Learning Concepts

Introduction to Machine Learning

Types of Machine Learning

Fundamentals of Machine Learning

Training and Testing

Features and Labels

Overfitting and Underfitting

Introduction to K-Nearest Neighbors (KNN)

Predictive Modelling Process

Classification Example using KNN

Implementation using Scikit-Learn

Module 3: Supervised Learning

Supervised Learning Fundamentals

Regression

Classification

Model Complexity & Generalization

Feature Scaling & Regularization

Model Training

Model Evaluation

Cross-Validation

Model Optimization

Advanced Supervised Learning

Data Leakage and Best Practices

Module 4: Unsupervised Learning

Unsupervised Learning Fundamentals

Clustering Fundamentals

K-Means Clustering

Measuring Similarity and Distance

Document Retrieval – Case Study in Clustering

Cluster Evaluation

Real-world Applications of Unsupervised Learning

Module 5: Recommendation Systems

Recommendation Systems Fundamentals

Recommending Products Using Machine Learning

Introduction to Deep Learning

Artificial Neural Networks (ANN)

Deep Learning Applications

Introduction to TensorFlow

Unsupervised Learning with Deep Learning

Module 6: Introduction to Python

Introduction to Python

Python vs Other Programming Languages

Python Versions

Python Installation and Setup

Python IDLE (Integrated Development and Learning Environment)

Executing Python Programs

Writing Your First Python Program

Module 7: Python Programming for Machine Learning

Python Basics: Keywords, Identifiers and Statements

Variables and Data Types

Decision Making and Loops

Functions in Python

Modules and Packages

Exception Handling

Files and Directories Handling

Classes and Objects (Object-Oriented Programming)

Regular Expressions

Socket Programming

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