AI Basic Concepts
Cut through the buzzwords. Understand what AI, machine learning, deep learning, and generative AI actually are — and how they fit together.
About this path
A no-jargon introduction to the ideas powering today's AI revolution. Built for anyone who reads "AI" and "ML" in headlines and wants to know what they really mean. By the end, you will understand how machines actually learn from data, what neural networks do under the hood, why ChatGPT predicts one word at a time, and where to draw honest lines around what AI can and cannot do. Designed for product people, students, founders, curious developers — anyone who wants the conceptual map before the code.
Path outline
Module 1
What AI Actually Is
Strip away the marketing. Understand what fits inside the AI umbrella, the levels of intelligence, and why this moment is different.
- 1 AI Defined: A Field, Not a Product 13 min
- 2 AI vs ML vs Deep Learning vs Generative AI 9 min
- 3 Narrow AI vs General AI: What We Have, What We Don't 10 min
Module 2
How Machines Actually Learn
Three flavors of learning, the role of data, and what "training" really means.
- 1 The Three Flavors: Supervised, Unsupervised, Reinforcement 12 min
- 2 What "Training" Actually Means 14 min
- 3 The #1 Pitfall: Overfitting 12 min
Module 3
Neural Networks Under the Hood
See the math-without-the-math behind the algorithms that power modern AI.
- 1 What Is a Neuron, Really? 19 min
- 2 How Networks Actually Learn: Gradient Descent 21 min
Module 4
Generative AI & Large Language Models
How ChatGPT, Claude, and Gemini actually work — and what they really are.
- 1 What Generative AI Actually Generates 9 min
- 2 How LLMs Work: One Token at a Time 8 min
- 3 Hallucinations: Why LLMs Confidently Lie 11 min
Module 5
AI in the Real World: Ethics, Limits, and What Comes Next
Bias, jobs, regulation, and the honest map of where AI is heading.
- 1 Bias and Fairness: When AI Inherits Our Prejudices 18 min
- 2 Honest Limits: What AI Cannot Do 13 min
- 3 Where to Go From Here 8 min