AI Foundations

Core AI, machine learning, deep learning, generative AI, embeddings, vector databases, RAG, agents, and enterprise AI architecture.

Start with the first article and continue in order. This page defines the Previous and Next flow for every article in this subcategory.

Learning Path

1. What Is Artificial Intelligence (AI)?
2. Types of AI: ANI, AGI, and ASI Explained
3. AI vs Machine Learning vs Deep Learning
4. Data: The Fuel of AI
5. Supervised Learning Explained
6. Unsupervised Learning Explained
7. Reinforcement Learning Basics
8. Features, Labels & Training Data Explained
9. Model Training, Validation & Testing Explained
10. Regression Algorithms Explained
11. Classification Algorithms Explained
12. Decision Trees and Random Forest Explained
13. Clustering and K-Means Explained
14. Recommendation Systems Explained
15. Bias, Variance and Overfitting Explained
16. Model Evaluation Metrics Explained
17. Neural Networks Explained
18. Activation Functions Explained
19. Forward Propagation and Backpropagation Explained
20. CNN for Image Processing
21. RNN and LSTM for Sequence Data
22. What Is Generative AI?
23. Transformer Architecture
24. Embeddings Explained
25. Tokenization Explained
26. Vector Databases
27. Large Language Models (LLMs)
28. Prompt Engineering
29. RAG: Retrieval Augmented Generation
30. AI Agents and MCP
31. Enterprise AI Architecture

Recommended Flow

1. Read the lessons in order from top to bottom.
2. Build a small example for the concepts that include implementation work.
3. Capture design decisions, risks, and production checks as you move forward.
4. Return to the AI Learning Path when this module is complete.

Outcome

By the end of AI Foundations, you should understand the module vocabulary, architecture tradeoffs, production concerns, and how this section connects to the broader AI roadmap.