PATH 01
Foundations
Rules, search, constraints, and planning—the ideas that make intelligent problem-solving explicit.
→Tutorial index
Tutorials
Explore practical tutorials that connect the underlying theory to a small, observable problem and a credible real-world application.
Learning paths
Understand AI. Build with it. Ship it safely.
Start with the ideas behind intelligent systems, then move through practical agent building and the engineering discipline required to put an AI-built application into production.
Understand AI
PATH 02
Core Machine Learning
Prediction, classification, clustering, dimensionality reduction, and ensemble methods grounded in data.
→PATH 03
Deep Learning
Perceptrons, convolution, sequence models, attention, and transfer learning through working examples.
→PATH 04
Advanced & Generative AI
A planned systems path for embeddings, retrieval, evaluation, tools, agents, guardrails, and deployment.
→Build and ship
PATH 05
Build With AI Agents
Move from a clear idea to a working prototype with repository instructions, coding agents, and tested implementation loops.
→PATH 06
Ship to Production
Make an AI-built application reliable with security checks, tests, observability, and the operational judgment production requires.
→All published tutorials
Linear Regression: Predicting Trends with Simple Math
House Price Prediction → Sales Forecasting Why Linear Regression? Imagine you’re a real estate agent trying to estimate house prices. You know that bigger…
Naïve Bayes: Fast, Interpretable Text Classification
SMS Spam Filter → Email Spam Detection What You’ll Build We’ll train a Naïve Bayes classifier that flags spam versus ham (legitimate) messages. You’ll…
k-Nearest Neighbors (k-NN): Learning from Neighbors
Iris Classification → Product Recommendation Why k-NN? Imagine moving into a new neighborhood. To understand what life is like, you look at your closest…
Decision Trees: Simple Logic, Powerful Predictions
Play Tennis → Loan Approval Prediction Why Decision Trees? Think about how you make decisions in everyday life. For instance, a bank loan officer…