Free Course
AI Fundamentals
Comprehensive course on AI Fundamentals
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Course curriculum
1 sections · 55 lessons · 9h total
Main Curriculum
What Is Agentic Coding? How AI Agents Modernize Code
10:21
Why AI Models Pause to Think: Test Time Compute Explained
10:32
Multi AI Agent Systems: When One AI Brain Isn’t Enough
10:55
The Four Types of Memory Every AI Agent Needs
10:41
Graph Neural Networks Explained: A Clear Guide to GNN Basics & Models
16:47
Five AI Risks That Can Get You Fired—And How to Avoid Them
10:58
CAG vs Long Context: How AI Models Use and Remember Information
10:59
MCP vs ADK: How Modern AI Agents Connect and Work Together
14:11
Why Agentic AI Fails: Infinite Loops, Planning Errors, and More
12:45
Predictive vs Generative AI: How They Work and When to Use Each
11:59
ADK vs RAG: How to Choose the Right AI Stack
6:31
What AI Agent Skills Are and How They Work
12:25
The 7 Skills You Need to Build AI Agents
14:37
What is Physical AI? How Robots Learn & Adapt in Real Life
8:52
What is AI Technical Debt? Key Risks for Machine Learning Projects
13:33
What is Multimodal AI? How LLMs Process Text, Images, and More
9:15
What is Human In The Loop with AI? How HITL Shapes AI Systems
10:44
A2A vs MCP: AI Agent Communication Explained
11:46
What Is NeuroSymbolic AI? Bridging Reasoning & Neural Networks
5:47
What is Multimodal RAG? Unlocking LLMs with Vector Databases
11:13
What is Prompt Caching? Optimize LLM Latency with AI Transformers
9:06
AI Agents vs. LLMs: Choosing the Right Tool for AI Tasks
7:51
Understanding AI Concepts: Machine Learning, Gen AI, NLP, & More
6:21
Escape the AI Graveyard: Fixing Data and Machine Learning Failures
7:29
AI Periodic Table Explained: Mapping LLMs, RAG & AI Agent Frameworks
16:51
AI Trends 2026: Quantum, Agentic AI & Smarter Automation
11:39
RAG vs Agentic AI: How LLMs Connect Data for Smarter AI
10:01
A Brief History of AI: From Machine Learning to Gen AI to Agentic AI
12:54
What Is an AI Stack? LLMs, RAG, & AI Hardware
9:06
Machine Learning Explained: A Guide to ML, AI, & Deep Learning
10:39
MCP vs gRPC: How AI Agents & LLMs Connect to Tools & Data
10:35
The Limits of AI: Generative AI, NLP, AGI, & What’s Next?
19:51
7 AI Terms You Need to Know: Agents, RAG, ASI & More
11:04
MCP vs API: Simplifying AI Agent Integration with External Data
13:11
RAG vs Fine-Tuning vs Prompt Engineering: Optimizing AI Models
13:10
AI, Machine Learning, Deep Learning and Generative AI Explained
10:01
Ten Everyday Machine Learning Use Cases
7:07
Large Language Models: How Large is Large Enough?
6:52
What Makes Large Language Models Expensive?
19:20
Generative AI: A Conversation with Malcolm Gladwell & Darío Gil
29:18
AI and Large Language Models Boost Language Translation
6:19
The 7 Types of AI - And Why We Talk (Mostly) About 3 of Them
6:50
The Rise of Generative AI for Business
14:45
Machine Learning vs. Deep Learning vs. Foundation Models
7:27
How to Add AI to Your Apps Faster with Embedded AI
7:35
Top 5 AI Myths
6:58
AI vs Machine Learning
5:49
What are Generative AI models?
8:47
Artificial Intelligence - Are We There Yet?
7:08
NLP vs NLU vs NLG
6:48
Humans vs. AI: Who should make the decision?
8:57
Artificial Intelligence vs. Augmented Intelligence
5:52
What is NLP (Natural Language Processing)?
9:38
What is Machine Learning?
8:23
What is a Chatbot?
9:42
Course description
Comprehensive course on AI Fundamentals
What you'll learn
Professional skill mastery with structured, progressive lessons
Hands-on real-world projects to build your portfolio
Industry best practices used at top-tier companies
Expert-level techniques that set you apart from peers
Certificate of completion to showcase your achievement
Lifetime access with all future updates included
Requirements
No prior experience required. A computer with internet access and a willingness to learn is all you need.
Your instructor
PI
Prof. IBM Technology and IBM Developer
Senior Practitioner & Educator
12+ years exp.
3 courses
2,017 students
A seasoned professional with years of hands-on industry experience. Their teaching philosophy centres on practical, no-nonsense instruction that bridges theory and real-world application.
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