Learning AI & Machine Learning
AI (Artificial Intelligence) is a field no engineer can ignore in 2026. With generative AI tools like ChatGPT, Claude, and Gemini now part of everyday workflows, understanding what AI is doing and how it works has become a real advantage for engineers.
This section covers everything from AI and machine learning fundamentals to generative AI, RAG, fine-tuning, evaluation, AI agents, and MCP — the protocol that lets AI tools connect to external services — all in a way that builds knowledge step by step.
What I Cover Here
Section titled “What I Cover Here”Learn how AI, machine learning, and deep learning relate to each other, and understand the core concepts behind each.
- What Is Machine Learning? — How machines learn from data, and how this differs from traditional programming
- What Is Deep Learning? — How neural networks work, and an overview of LLMs (Large Language Models)
- Learning Paradigms — Transfer learning, fine-tuning, federated learning, and other modern techniques
Learn the foundations needed to use generative AI well: LLMs, reasoning models, prompt engineering, context engineering, and harness engineering.
- What Is Generative AI? — The big picture of AI that generates text, images, music, and video
- What Is an LLM? — How large language models work and developed
- Generative AI Models and Intelligence Metrics — Model types, IQ-style scores, and practical capability signals
- Harness Engineering — Connecting AI to tools, permissions, and checks
Learn how RAG lets LLMs search external documents and answer from evidence. This is the foundation for safely connecting AI to internal documents, current information, and specialized sources.
- What Is RAG? — RAG flow, architecture, evaluation, and current best practices
- RAG History — The path from search and QA systems to modern RAG
- RAG Architecture Patterns — Naive RAG, Advanced RAG, Graph RAG, and when to use each
- Agentic RAG — Search planning, re-search, and verification handled by agents
- Code RAG and Coding Agents — Repository search, structured chunks, and test execution
- Embeddings and Vector Representation — How text becomes vectors for semantic search
- Retrieval Strategies — BM25, vector search, hybrid search, and reranking
- Chunking Strategy — How to split documents into retrievable units
- Choosing a Vector Database — Comparing vector stores for RAG systems
Learn how additional training changes an LLM’s behavior, style, and task performance when prompting or RAG is not enough.
- What Is Fine-tuning? — Core concepts and when to choose fine-tuning versus RAG or prompts
- Fine-tuning Methods — Full Fine-tuning, LoRA, QLoRA, and Adapter layers
- Training Data Preparation — Collecting, formatting, and checking training data
- Knowledge Distillation — Transferring large-model behavior into smaller models
Learn how to measure model and LLM-application quality before production use.
- What Is AI Evaluation? — Automated evaluation, human evaluation, and LLM-as-a-Judge
- AI Evaluation Frameworks — LM Evaluation Harness, Ragas, DeepEval, and related tools
- Business Fit Evaluation — Task completion, cost, and ROI in real workflows
- Consistency & Reliability Evaluation — Testing whether outputs stay stable and dependable
- Human-in-the-Loop Evaluation — Where human judgment belongs in evaluation pipelines
- Safety Evaluation — Harmful output, prompt injection, and leakage risk checks
Learn how AI systems use tools, plan work, and complete multi-step tasks.
- What Is an AI Agent? — Core components and the ReAct loop
- Agent Frameworks — LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, and other options
- Orchestration — Designing multi-agent and workflow coordination
- AI Agents and MCP — How MCP connects agents to external tools
Learn about MCP — the open standard that lets AI models connect with external tools and data. Understanding MCP explains why tools like Claude Code and Claude Desktop can integrate with so many services.
- What Is MCP? — An overview of the protocol that bridges AI and external tools
- Why MCP? — The M×N integration problem and how MCP solves it
- MCP Architecture — The three-layer structure: Host, Client, and Server
- MCP Capabilities — Tools, Resources, and Prompts — what they are and when to use each
Learn the decision-making structures, policies, and processes for using AI safely, ethically, and legally. Covers major frameworks including NIST AI RMF, EU AI Act, and ISO/IEC 42001, and explores agile governance approaches for the fast-moving generative AI era.
- What Is AI Governance? — Core concepts, major frameworks, and the three layers of governance
- Agile AI Governance — Practical approaches including Living Policy, sprint-based risk management, and continuous compliance
Learn the five core principles — fairness, transparency, explainability, privacy, and accountability — for developing, operating, and using AI ethically and responsibly. Covers major regulations and approaches including EU AI Act, NIST AI RMF, and Constitutional AI.
- What Is Responsible AI? — The five core principles, types of AI bias, and an overview of EU AI Act and NIST AI RMF
Understand the security risks specific to generative AI and learn systematically from attack techniques through defense frameworks and guardrail implementation. Covers the knowledge developers need when embedding AI into products.
- Major Attack Techniques — How prompt injection, jailbreaking, data poisoning, and model inversion attacks work
- Security Frameworks — Comparing OWASP LLM Top 10, NIST AI 600-1, and MITRE ATLAS
- OWASP Agentic AI Framework — 10 threat categories and mitigations specific to AI agents
- Agent Security — Tool misuse, context poisoning, and MCP security design
- Guardrails: Concepts and Implementation — Input, output, and execution guard design patterns with implementation examples
Go beyond AI and ML technical understanding to learn AI adoption at the organizational and business level. Structured across five phases: strategy, readiness, organization building, execution, and sustainment.
- What Is AI Transformation? — Definition, background, differences from DX, and why 70% of companies stay at “usage” without transforming
- AI Maturity Model — A five-stage diagnostic synthesis informed by Gartner, IBM, and NIST
- What Is an AI COE? — Role, structure, and how to launch a Center of Excellence for AI
- Preventing PoC Stalls — Production-Backward design and countering the five root causes
- Scaling AI — Overcoming technical and organizational barriers from pilot to enterprise rollout
Suggested Learning Order
Section titled “Suggested Learning Order”Starting with AI & machine learning basics
Section titled “Starting with AI & machine learning basics”Begin with What Is Machine Learning? to get a clear picture of how AI, ML, and deep learning relate before diving into each topic.
- What Is Machine Learning? — AI vs. ML, and the three types of learning
- What Is Deep Learning? — Neural networks and how LLMs work
- Learning Paradigms — Practical techniques like transfer learning and fine-tuning
Using generative AI in practical work
Section titled “Using generative AI in practical work”Start with how LLMs work, then move through model choice, prompts, context, harnesses, and RAG. This path connects chat-based use to practical system design.
- What Is Generative AI? — The overall picture
- What Is an LLM? — LLM basics
- Generative AI Models and Intelligence Metrics — How to choose models
- Prompt Engineering — Instruction design
- Context Engineering — Supplying needed information
- Harness Engineering — Execution and verification design
- What Is RAG? — Evidence-grounded generation from external documents
- What Is AI Evaluation? — Measuring quality before production use
Learning MCP for practical AI tool use
Section titled “Learning MCP for practical AI tool use”If you want to get more out of tools like Claude Code, start with What Is MCP?.
- What Is MCP? — Core concepts and overview
- Why MCP? — The problem it solves
- MCP Architecture — How it works under the hood
- MCP Capabilities — What you can actually do with it
Prerequisites
Section titled “Prerequisites”No advanced math is required. Having a basic sense of functions and probability from high school math will help, but it is not necessary to follow along with the conceptual explanations.
Familiarity with engineering basics (terminal, Git) is helpful but not required. Reading Engineering Basics first will give you a more practical foundation.