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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