Series Introduction – From Automation to AI: A Practitioner's Journey

From Automation to AI – A Practitioner’s Journey

Chapter 0.1 – Why Automation Engineers Should Learn AI (And What NOT to Learn First)

Why Automation Engineers Should Learn AI (And What NOT to Learn First)

Chapter 0.2 – My Background and Learning Strategy: Approaching AI as a Solution Architect

Chapter 1.1 – What Is AI (Really?)

Cutting Through the AI Hype

Chapter 1.2 – How Machines Learn

Chapter 1.3 – Types of Machine Learning

Picking the Right Tool for Your Data

Chapter 2.0 – ML Data Foundations: The Bridge

Chapter 2.1 – Data: The New Configuration File

Chapter 2.2 – Features, Labels, and Models

Chapter 2.3 – Model Training vs Execution

Chapter 3.0 – The ML Project Workflow

Chapter 3.1 – Common ML Algorithms (Intuition Only)

Understanding the Tools in the Toolbox

Chapter 3.2 – Overfitting & Underfitting: When Models Break in Production

Chapter 3.3 – Model Evaluation: The Metric That Fooled Me

The Dashboard That Lied

Chapter 3.4 – Feature Engineering: The Real Work Behind ML

The Hidden Truth About Machine Learning

Chapter 4.0 – Deep Learning Roadmap: The Bridge

Chapter 4.1 – Why Deep Learning Exists

Why Deep Learning Exists

Chapter 4.2 – Neural Networks Explained Like Infrastructure

Chapter 4.3 – Deep Learning Architectures: CNNs, RNNs & Practical Examples

Chapter 4.4 – Transformers and Modern Architectures

Chapter 5.0 – Generative AI & LLM Roadmap

Chapter 5.1 – What Is Generative AI

What Is Generative AI?

Chapter 5.2 – How Large Language Models Are Trained (High Level)

How Large Language Models Are Trained (High Level)

Chapter 5.3 – Prompt Engineering for Engineers

Chapter 5.4 – Prompt Engineering in Practice: Workflow & Effective Patterns

Chapter 5.5 – Tokens, Context Windows, and Why Prompts Matter

Chapter 5.6 – Advanced Prompt Chaining and Orchestration

Advanced Prompt Chaining and Orchestration