Master Study AI

Foundations of Artificial Intelligence

artificial-intelligence-ai.

Course Modules:

Module 1: What is Artificial Intelligence?

Definitions and origins of AI

Weak AI vs. Strong AI

AI vs. Machine Learning vs. Deep Learning

Module 2: The Evolution of AI

Milestones in AI development

Early systems, expert systems, and neural networks

AI in today’s world (2025 and beyond)

Module 3: Categories and Types of AI

Reactive machines vs. self-aware systems

Narrow AI vs. General AI

Applications across industries

Module 4: Core Concepts and Terminology

Agents and environments

Search and optimization

Probability, logic, and inference in AI

Module 5: Introduction to Machine Learning

Overview of supervised, unsupervised, and reinforcement learning

Common algorithms (decision trees, k-NN, SVM)

Data's role in AI systems

Module 6: Applications of AI in Real Life

AI in healthcare, finance, education, and retail

Chatbots, recommendation systems, computer vision

Ethics and impact of AI on society

Module 7: AI Ethics and Human-Centered Design

Bias in AI systems

Transparency and explainability

Aligning AI with human values

Module 8: Final Assessment

Course recap and quiz

Practical examples and mini-case studies

Certificate of completion and next steps

Tools & Technologies Used:

Visual tools and simulations

Optional: Google Colab for basic examples

Case studies and demos (no coding required for beginners)

Target Audience:

Beginners with no AI background

Students and educators in STEM and business

Non-technical professionals exploring AI applications

Anyone curious about the future of intelligent technology

Global Learning Benefits:

Build a strong foundation to explore AI specialties

Understand AI's role in modern industries and society

Prepare for advanced machine learning and deep learning courses

Make informed decisions about adopting AI in your work or projects

 

 

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