AI Basics – Foundation to Future

About Course

Welcome to “AI Basics – Foundation to Future”, a beginner-friendly course designed to make Artificial Intelligence understandable, practical, and exciting — whether you’re a student exploring AI for the first time, or a professional building your technical foundation.

The core concepts behind AI and its types
The essential math that powers intelligent machines
How to work with data and prepare it for learning
Machine learning and deep learning explained in the simplest way
The magic of NLP and AI’s impact in industries like healthcare, finance, marketing & more

We’ve designed each module with clear video lessons, real-world examples, and quizzes to reinforce your learning.

Whether you’re a college student, tech enthusiast, or career switcher — this course is your gateway to one of the most powerful technologies of the future.

Let’s unlock the world of AI — step by step.

Understand the core concepts of Artificial Intelligence
Learn the math behind AI — made simple and practical
Work with data: types, cleaning, and preprocessing
Explore the basics of Machine Learning & Deep Learning
Get introduced to Natural Language Processing (NLP)
Discover real-world applications of AI across industries
Understand AI ethics, bias, and the future of AI
Gain confidence to dive deeper into advanced AI & ML courses

Course Content

Module 1: Introduction to Artificial Intelligence
• What is AI? • Examples of AI • Types of AI (Narrow, General, Super) • Quiz

Introduction to Artificial Intelligence

01:56
Module 1 : Introduction to AI

Module 2: Mathematics for AI (Beginner-Friendly)
• Basics of Linear Algebra (Vectors, Matrices) • Probability • Logic and Sets

Module 3: Understanding Data
• Why Data is Important in AI? • Types of Data • Qualities of Good Data • What is Data Processing? • Tools Used for Data Processing.

Module 4: Machine Learning Fundamentals
• What is Machine Learning? • Types of ML: Supervised, Unsupervised, Reinforcement • Supervised Learning Flow • Unsupervised Learning Flow • Common ML Algorithms • Challenges in ML

Module 5: Deep Learning Simplified
• What is a Deep Learning? • How Deep Learning Works? • Types of Neural Network • Why use Deep Learning? • Popular Libraries.

Module 6: Basics of Natural Language Processing (NLP)
• What is NLP? • Key Task in NLP • NLP Importance • Tools and Libraries for NLP • How NLP model works • Limitations of NLP

Module 7: AI in the Real World
• AI in Healthcare (diagnostics, drug discovery) • AI in Finance (fraud detection, trading) • AI in Marketing and E-commerce • Government, education, and manufacturing use-cases

Module 8: Ethics, Risks & Future of AI
• Why Ethics matters? • Common ethical concerns • Example of Ethical Issues • Building Ethical AI • The future of AI

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AI Basics – Foundation to Future

About Course

What Will You Learn?

Course Content

Module 1: Introduction to Artificial Intelligence
• What is AI? • Examples of AI • Types of AI (Narrow, General, Super) • Quiz

Introduction to Artificial Intelligence

Module 1 : Introduction to AI

Module 2: Mathematics for AI (Beginner-Friendly)
• Basics of Linear Algebra (Vectors, Matrices) • Probability • Logic and Sets

Mathematics for AI

Module 2: Mathematics for AI

Module 3: Understanding Data
• Why Data is Important in AI? • Types of Data • Qualities of Good Data • What is Data Processing? • Tools Used for Data Processing.

Understading Data in AI

Understanding Data in AI

Module 4: Machine Learning Fundamentals
• What is Machine Learning? • Types of ML: Supervised, Unsupervised, Reinforcement • Supervised Learning Flow • Unsupervised Learning Flow • Common ML Algorithms • Challenges in ML

Machine Learning Fundamentals

Machine Learning Fundamentals

Module 5: Deep Learning Simplified
• What is a Deep Learning? • How Deep Learning Works? • Types of Neural Network • Why use Deep Learning? • Popular Libraries.

Deep Learning Simplified

Deep Learning Simplified

Module 6: Basics of Natural Language Processing (NLP)
• What is NLP? • Key Task in NLP • NLP Importance • Tools and Libraries for NLP • How NLP model works • Limitations of NLP

Basics of NLP

Basics of NLP

Module 7: AI in the Real World
• AI in Healthcare (diagnostics, drug discovery) • AI in Finance (fraud detection, trading) • AI in Marketing and E-commerce • Government, education, and manufacturing use-cases

AI in the Real World

AI in the Real World

Module 8: Ethics, Risks & Future of AI
• Why Ethics matters? • Common ethical concerns • Example of Ethical Issues • Building Ethical AI • The future of AI

Ethics, Risks & Future of AI

Ethics, Risks & Future of AI

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About Course

What Will You Learn?

Course Content

Module 1: Introduction to Artificial Intelligence • What is AI? • Examples of AI • Types of AI (Narrow, General, Super) • Quiz

Introduction to Artificial Intelligence

Module 1 : Introduction to AI

Module 2: Mathematics for AI (Beginner-Friendly) • Basics of Linear Algebra (Vectors, Matrices) • Probability • Logic and Sets

Mathematics for AI

Module 2: Mathematics for AI

Module 3: Understanding Data • Why Data is Important in AI? • Types of Data • Qualities of Good Data • What is Data Processing? • Tools Used for Data Processing.

Understading Data in AI

Understanding Data in AI

Module 4: Machine Learning Fundamentals • What is Machine Learning? • Types of ML: Supervised, Unsupervised, Reinforcement • Supervised Learning Flow • Unsupervised Learning Flow • Common ML Algorithms • Challenges in ML

Machine Learning Fundamentals

Machine Learning Fundamentals

Module 5: Deep Learning Simplified • What is a Deep Learning? • How Deep Learning Works? • Types of Neural Network • Why use Deep Learning? • Popular Libraries.

Deep Learning Simplified

Deep Learning Simplified

Module 6: Basics of Natural Language Processing (NLP) • What is NLP? • Key Task in NLP • NLP Importance • Tools and Libraries for NLP • How NLP model works • Limitations of NLP

Basics of NLP

Basics of NLP

Module 7: AI in the Real World • AI in Healthcare (diagnostics, drug discovery) • AI in Finance (fraud detection, trading) • AI in Marketing and E-commerce • Government, education, and manufacturing use-cases

AI in the Real World

AI in the Real World

Module 8: Ethics, Risks & Future of AI • Why Ethics matters? • Common ethical concerns • Example of Ethical Issues • Building Ethical AI • The future of AI

Ethics, Risks & Future of AI

Ethics, Risks & Future of AI

Earn a certificate

Student Ratings & Reviews

Support

Module 1: Introduction to Artificial Intelligence
• What is AI? • Examples of AI • Types of AI (Narrow, General, Super) • Quiz

Module 2: Mathematics for AI (Beginner-Friendly)
• Basics of Linear Algebra (Vectors, Matrices) • Probability • Logic and Sets

Module 3: Understanding Data
• Why Data is Important in AI? • Types of Data • Qualities of Good Data • What is Data Processing? • Tools Used for Data Processing.

Module 4: Machine Learning Fundamentals
• What is Machine Learning? • Types of ML: Supervised, Unsupervised, Reinforcement • Supervised Learning Flow • Unsupervised Learning Flow • Common ML Algorithms • Challenges in ML

Module 5: Deep Learning Simplified
• What is a Deep Learning? • How Deep Learning Works? • Types of Neural Network • Why use Deep Learning? • Popular Libraries.

Module 6: Basics of Natural Language Processing (NLP)
• What is NLP? • Key Task in NLP • NLP Importance • Tools and Libraries for NLP • How NLP model works • Limitations of NLP

Module 7: AI in the Real World
• AI in Healthcare (diagnostics, drug discovery) • AI in Finance (fraud detection, trading) • AI in Marketing and E-commerce • Government, education, and manufacturing use-cases

Module 8: Ethics, Risks & Future of AI
• Why Ethics matters? • Common ethical concerns • Example of Ethical Issues • Building Ethical AI • The future of AI