Español Start learning

Courses

Course catalog

One course, taught as a route you actually walk. Below is the whole thing: every path, every lesson title, and the honest time each one takes.

5 stages, beginner to expert, plus 8 role tracks.

  • 32learning paths
  • 438lessons
  • 7,724screens in total
  • 135–210hours, end to end

Where do you want to get to?

Pick a destination, not a catalogue. Each one is a handful of courses with an honest estimate; everything below stays here for when you want it.

Foundations that endure, tools that evolve

We teach the principles slowly and update the tools quickly. 19 of the 32 courses teach things that were true before this generation of AI and will be true after it: computation, maths, control theory, systems, how a model actually decides. The other 13 track what is current — agents, AI coding tools, the techniques that move with the models, and the shape of a job this year. When the tools change we rewrite that layer. What is underneath keeps its value.

How long it takes

About 135–210 h to work through all of it.

Where the time goes. Each path carries its own figure on its card.
StagePathsLessonsScreensTime
Understand AI34466311.5–18 h
Computing & Math Foundations4501,04318.5–28 h
Machine Learning & Modern AI6771,26222.5–34.5 h
Building & Shipping AI5811,24622.5–34.5 h
Robotics & Physical AI61082,40640–65 h
Role tracks8781,10419.5–30 h
Whole catalog324387,724135–210 h

Nobody works through the whole catalog in one run, and nobody has to. The paths are independent, and the shortest of them is about an hour.

What a screen is

A lesson is a run of screens, and they are not all the same thing. This is the whole catalog broken down by what each screen asks of you.

  • 3,108 reading cardsOne idea each, in plain words, with a deeper layer folded underneath for anyone who wants it.
  • 2,101 questionsMultiple choice, asked before the teaching rather than after. A wrong answer gets the reason it was tempting, not a cross.
  • 1,475 exercises, in 61 formsSomething to move and a goal to reach: dials on a running simulation, a line to fit to real measurements, a fixed budget to split against a payoff meter, a program to assemble line by line.
  • 672 written challengesYou write the answer in your own words first, then read a worked one and mark your own against it.
  • 368 reflectionsA question with no right answer. What you write goes in a journal nobody else can reach.
One exercise, start to finish

In Control: From PID to MPC you get a cold room, a boiler that can only add heat, and two dials feeding it: one marked “Push harder the further off the room is”, the other “Push harder the longer it stays off”. Hold the room within 0.3 degrees of 21 for ten seconds. Turn the first as high as it goes and the room always parks short of target — which is the thing the lesson is really about, and the reason it is an exercise and not a paragraph.

Stage 1 of 5

Understand AI

Start from zero and build a real mental model of how AI works.

  • Beginner → Advanced
  • 3 paths
  • 44 lessons
  • 663 screens
  • 11.5–18 h

AI Literacy

Understand AI, and use it well.

What AI is, how it behaves, and how to work with it: what to hand over, how to check the answer, and what never to paste into it.

  • Beginner
  • 14 lessons
  • 222 screens
  • 3.5–5.5 h
Lessons (14)
  1. What Is AI? 17 min
  2. Machine Learning vs. Rules 13 min
  3. What Makes Chatbots Work? 17 min
  4. Tokens: How Text Becomes Numbers 19 min
  5. Neural Networks, Simply 14 min
  6. AI Strengths and Weaknesses 13 min
  7. Generative AI Beyond Chat 14 min
  8. What to Hand to AI 16 min
  9. Saying What You Mean 14 min
  10. Checking the Work 14 min
  11. Owning the Output 17 min
  12. Privacy and Data Handling 18 min
  13. When AI Acts for You 16 min
  14. Designing Your Own Evals 17 min

Machine Learning, Demystified

How machines actually learn.

The missing middle: how a model turns examples into skill. The three ways machines learn, features and labels, how training drives down error, why models overfit, how trial-and-error and feedback shape behavior, and how meaning becomes geometry in embeddings and vector search.

  • Intermediate
  • 10 lessons
  • 152 screens
  • 2.5–4 h
Lessons (10)
  1. How Machines Learn 18 min
  2. Learning From Examples 13 min
  3. Getting It Right 15 min
  4. Finding Hidden Structure 13 min
  5. Learning by Trial and Error 16 min
  6. Embeddings and Vector Search 14 min
  7. Deep Learning and Backpropagation 18 min
  8. Tensors and How Models Train 16 min
  9. Computer Vision and CNNs 16 min
  10. Bias, Variance, and Regularization 18 min

How AI Really Works

The fundamentals that never expire.

Go deeper than the headlines: how AI learns from data, decides what matters, becomes a helpful assistant, gets tested, and why "do what I meant" is the hardest problem of all.

  • Advanced
  • 20 lessons
  • 289 screens
  • 5.5–8.5 h
Lessons (20)
  1. Data Is the Teacher 18 min
  2. Attention, Simply 18 min
  3. From Predictor to Assistant 17 min
  4. Measuring Intelligence 16 min
  5. What We Said vs. What We Meant 17 min
  6. The Transformer, In Depth 16 min
  7. Scaling Laws and Emergence 15 min
  8. Fine-Tuning, LoRA, and Adaptation 16 min
  9. Tool Use and Function Calling 19 min
  10. The Hugging Face Hub 15 min
  11. The Core Libraries 15 min
  12. Fine-Tuning on One GPU 17 min
  13. Training and Shipping It 21 min
  14. How Reasoning Models Think 15 min
  15. Learning That Never Stops 17 min
  16. Knowing What You Don't Know 17 min
  17. Free Energy and Active Inference 16 min
  18. The Bitter Lesson 15 min
  19. The Open Frontier 16 min
  20. Final Exam: Understand AI 14 min

Stage 2 of 5

Computing & Math Foundations

The code and math every serious AI builder leans on.

  • Beginner → Intermediate
  • 4 paths
  • 50 lessons
  • 1043 screens
  • 18.5–28 h

Coding Foundations

Think like a builder.

You don't need to memorize syntax: AI writes most of it now. Learn to think like a builder and to read and review what AI produces.

  • Beginner
  • 10 lessons
  • 189 screens
  • 3–5.5 h
Lessons (10)
  1. Variables and Decisions 23 min
  2. Loops and Patterns 20 min
  3. Functions as Building Blocks 20 min
  4. APIs and Apps 17 min
  5. Debugging Like a Builder 14 min
  6. Reading Code You Didn't Write 18 min
  7. Specifying and Verifying Work 20 min
  8. The Practical Dev Toolkit 22 min
  9. Full-Stack, End to End 25 min
  10. Frontend with TypeScript and React 24 min

Python, Properly

The one language AI work runs on.

AI writes most of the code now, so your edge is reading, running, and judging it. Get genuinely fluent in real Python and its data and AI stack: types, collections, files and JSON, environments, NumPy and pandas, calling APIs, and testing - hands-on, project-first.

  • Beginner
  • 12 lessons
  • 287 screens
  • 4.5–7.5 h
Lessons (12)
  1. Why Python (and How to Read It) 27 min
  2. Values, Variables, and Types 26 min
  3. Lists, Dicts, Sets, and Tuples 28 min
  4. Comprehensions and Pythonic Style 24 min
  5. Functions, Arguments, and Modules 25 min
  6. Files, JSON, and the Standard Library 26 min
  7. Errors, Exceptions, and Tracebacks 25 min
  8. Virtual Environments and pip 23 min
  9. NumPy and pandas: the Data Duo 24 min
  10. Calling APIs in Python: requests and async 24 min
  11. Classes, Type Hints, and Testing 26 min
  12. Building APIs with FastAPI 21 min

Computer Science Foundations

The bedrock under all of it.

The durable fundamentals of how computers and algorithms actually work, the kind that make you a sharp reviewer and director of AI rather than syntax trivia: data structures, algorithms and Big-O, operating systems, how the internet works, databases and SQL, concurrency, version control with Git, and software-engineering practice.

  • Intermediate
  • 18 lessons
  • 282 screens
  • 5–8 h
Lessons (18)
  1. How Computers Actually Work 17 min
  2. Data Structures 19 min
  3. Memory, Caches, and Locality 16 min
  4. Algorithms and Big-O 18 min
  5. Dynamic Programming 16 min
  6. Graph Algorithms 16 min
  7. Operating Systems 19 min
  8. How the Internet Works 14 min
  9. Databases and SQL 18 min
  10. Concurrency and Parallelism 16 min
  11. Version Control with Git 20 min
  12. Software Engineering Practices 18 min
  13. Classical AI: Search and Planning 16 min
  14. Logic and Knowledge Representation 16 min
  15. Computability and P vs NP 16 min
  16. Compilers and Interpreters 16 min
  17. Coding Interview Patterns 20 min
  18. Coding Interview Problems, Worked 21 min

Math for Machine Learning

The language under the hood.

The math that powers everything: vectors and matrices, derivatives and gradients, probability and statistics, optimization, and the information theory behind loss functions: taught with analogies, not intimidation.

  • Intermediate
  • 10 lessons
  • 285 screens
  • 5–7.5 h
Lessons (10)
  1. Vectors and Vector Spaces 31 min
  2. Matrices as Transformations 31 min
  3. Derivatives and Gradients 33 min
  4. Why Small Samples Lie 29 min
  5. Probability Essentials 33 min
  6. Statistics and Distributions 40 min
  7. Optimization: Finding the Bottom 29 min
  8. Entropy and Cross-Entropy 30 min
  9. Differential Equations and Numerical Methods 29 min
  10. Final Exam: Computing and Math Foundations 15 min

Stage 3 of 5

Machine Learning & Modern AI

From classic algorithms to language, vision, and generative models.

  • Intermediate
  • 6 paths
  • 77 lessons
  • 1262 screens
  • 22.5–34.5 h

Classical Machine Learning

The algorithms that still win.

The workhorse algorithms every ML interview asks about: linear and logistic regression, decision trees, random forests and boosting, SVMs and k-NN, clustering and PCA, and the feature engineering that decides who wins.

  • Intermediate
  • 9 lessons
  • 148 screens
  • 2.5–4.5 h
Lessons (9)
  1. Linear Regression 22 min
  2. Logistic Regression and Classification 22 min
  3. Working with Data 17 min
  4. Evaluating a Model 17 min
  5. Decision Trees 17 min
  6. Random Forests and Gradient Boosting 19 min
  7. SVMs and k-Nearest Neighbors 17 min
  8. Clustering and PCA 17 min
  9. Feature Engineering and Model Selection 17 min

Machine Learning Deep Dive

The rigorous, advanced core.

The graduate-level core: learning theory and generalization, loss functions, regularization, rigorous evaluation, probabilistic and Bayesian ML, reinforcement learning in depth, recommenders, time series, interpretability, AutoML, graph ML, and causal inference.

  • Advanced
  • 19 lessons
  • 318 screens
  • 5.5–8.5 h
Lessons (19)
  1. Learning Theory and Generalization 20 min
  2. Loss Functions and What They Encode 22 min
  3. Regularization, In Depth 20 min
  4. Model Evaluation, Rigorously 16 min
  5. Probabilistic and Bayesian ML 20 min
  6. Reinforcement Learning, In Depth 18 min
  7. Recommender Systems 18 min
  8. Time Series and Forecasting 18 min
  9. Interpretability and Explainability 20 min
  10. Hyperparameter Tuning and AutoML 16 min
  11. Graph Machine Learning 17 min
  12. Causal Inference 17 min
  13. Weight Initialization 17 min
  14. Batch and Layer Normalization 16 min
  15. Variational Autoencoders 18 min
  16. Algorithmic Fairness and Bias Auditing 16 min
  17. Bayesian Networks and Graphical Models 15 min
  18. Mechanistic Interpretability 19 min
  19. Neural Operators 16 min

Natural Language Processing

Teaching machines to read and write.

How AI handles human language: cleaning and counting text, word vectors, language modeling and perplexity, RNNs and LSTMs, seq2seq and attention, BERT vs GPT, and how to score every NLP task.

  • Intermediate
  • 8 lessons
  • 133 screens
  • 2–3.5 h
Lessons (8)
  1. How Language AI Works 14 min
  2. Text as Data 16 min
  3. Word Vectors: word2vec and GloVe 15 min
  4. Language Modeling and Perplexity 16 min
  5. RNNs, LSTMs, and Memory 19 min
  6. Seq2Seq, Attention, and Translation 24 min
  7. BERT vs GPT: Two Ways to Pretrain 16 min
  8. NLP Tasks and How to Score Them 19 min

Computer Vision

Teaching machines to see.

The deepest track: from pixels and classic filters to CNNs and architectures, object detection and segmentation, vision transformers, 3D geometry, generative and multimodal vision, video understanding, and shipping vision in production.

  • Intermediate
  • 17 lessons
  • 274 screens
  • 4.5–7.5 h
Lessons (17)
  1. How Computers See 15 min
  2. Image Formation and Color 17 min
  3. Edges, Filters, and Classic Features 19 min
  4. Convolutional Networks, In Depth 16 min
  5. CNN Architectures: AlexNet to ResNet 18 min
  6. Image Classification in Practice 17 min
  7. Object Detection: R-CNN to YOLO 21 min
  8. Segmentation: Labeling Every Pixel 17 min
  9. Pose Estimation and Tracking 20 min
  10. Vision Transformers 20 min
  11. 3D Vision and Geometry 18 min
  12. Generative Vision: GANs, VAEs, Diffusion 15 min
  13. Self-Supervised and Multimodal Vision 15 min
  14. Video Understanding 18 min
  15. Computer Vision in Production 15 min
  16. Signals and the Fourier View 14 min
  17. Bird's-Eye-View and Occupancy Networks 22 min

Video Generation AI

How AI dreams up moving pictures.

How modern video models work, from diffusion fundamentals and latent diffusion to text-to-video, cascaded architectures, diffusion transformers and Sora-style world models, controllability, and the limits and ethics.

  • Advanced
  • 9 lessons
  • 139 screens
  • 2.5–4 h
Lessons (9)
  1. How AI Generates Video 18 min
  2. Diffusion Models, Properly 18 min
  3. Flow Matching and Rectified Flow 12 min
  4. Latent Diffusion and Conditioning 16 min
  5. Text-to-Video: Adding Time 14 min
  6. Video Diffusion Architectures 23 min
  7. Diffusion Transformers and Sora 16 min
  8. Controlling Generated Video 17 min
  9. Evaluating Video AI and Its Limits 20 min

LLMs & Multimodal AI

Building and steering the big models.

Inside modern LLMs and beyond: how they work, training from scratch, supervised fine-tuning, RLHF and DPO, PEFT and quantization, advanced RAG, hallucination control, multimodal LLMs, audio and speech, and any-to-any multimodal AI.

  • Advanced
  • 15 lessons
  • 250 screens
  • 4.5–7 h
Lessons (15)
  1. Inside a Modern LLM 18 min
  2. Training a Model From Scratch 16 min
  3. Supervised Fine-Tuning 20 min
  4. RLHF, DPO, and Preference Tuning 21 min
  5. PEFT, LoRA, and Quantization 21 min
  6. Advanced RAG 19 min
  7. Hallucination Control and Grounding 20 min
  8. Multimodal LLMs 18 min
  9. Audio and Speech AI 16 min
  10. Building Voice Interfaces: Wake Word to Reply 19 min
  11. Any-to-Any Multimodal AI 17 min
  12. Mixture of Experts 18 min
  13. Speculative Decoding 18 min
  14. Project: Fine-Tune a Small Model 17 min
  15. Final Exam: Machine Learning and Modern AI 15 min

Stage 4 of 5

Building & Shipping AI

Turn models into real, safe, production-grade products.

  • Advanced
  • 5 paths
  • 81 lessons
  • 1246 screens
  • 22.5–34.5 h

Prompt Engineering & AI Collaboration

Get the most out of any model.

The skill that defines working with AI: prompting foundations, few-shot and chain-of-thought, structured outputs, patterns and anti-patterns, designing tools and function calls, context engineering, eval-driven development, agentic workflows, and AI in production.

  • Advanced
  • 9 lessons
  • 137 screens
  • 2–4 h
Lessons (9)
  1. Prompting Foundations 14 min
  2. Few-Shot and Chain-of-Thought 13 min
  3. Structured Outputs and Contracts 15 min
  4. Prompt Patterns and Anti-Patterns 17 min
  5. Designing Tools and Function Calls 15 min
  6. Context Engineering 17 min
  7. Eval-Driven Prompt Development 17 min
  8. Designing Agentic Workflows 18 min
  9. Working With AI Systems in Production 18 min

Building With AI

From prompts to a fleet of agents.

The advanced track: modern prompt engineering, briefing long-running agents, building one from scratch, and running AI coding tools like a pro: context, worktrees, and all.

  • Advanced
  • 18 lessons
  • 276 screens
  • 5–8 h
Lessons (18)
  1. Prompt Engineering, Properly 14 min
  2. Briefing a Long-Running AI 13 min
  3. Build an Agent From Scratch 20 min
  4. Context Mastery 14 min
  5. Giving AI Memory: Context and State 17 min
  6. Claude Code, Fundamentals 22 min
  7. The Everyday AI Dev Workflow 16 min
  8. Parallel Agents and Worktrees 19 min
  9. Multi-Agent Orchestration 21 min
  10. RAG in Practice 14 min
  11. MCP and Tool Integration 16 min
  12. Build Your First MCP Server 19 min
  13. Wiring AI Into the World: APIs and Automation 17 min
  14. Connecting Accounts: OAuth and Integrations 15 min
  15. Choosing and Routing Models 17 min
  16. Defending Your AI App 18 min
  17. Code Review in the AI Age 14 min
  18. Agent-First Software Design 17 min

Production AI & MLOps

From notebook to product.

Everything between a trained model and a reliable product: the ML lifecycle, data pipelines, training at scale, serving and deployment, monitoring and drift, experiment tracking and CI/CD, and serving LLMs in production.

  • Advanced
  • 21 lessons
  • 324 screens
  • 5.5–8.5 h
Lessons (21)
  1. The ML Lifecycle 17 min
  2. Data Engineering and Pipelines 15 min
  3. Training at Scale 15 min
  4. Serving and Deployment 16 min
  5. Monitoring and Drift 18 min
  6. Experiments, Versioning, and CI/CD 15 min
  7. LLMOps: Serving LLMs in Production 17 min
  8. A/B Testing and Online Experiments 16 min
  9. The Economics of AI Systems 15 min
  10. The Data Engine 15 min
  11. The Reliability Gap 15 min
  12. Designing for Failure 16 min
  13. Inference Optimization 18 min
  14. From Demo to Fleet 17 min
  15. When One Machine Runs Out 13 min
  16. The Flavors of Parallelism 16 min
  17. Talking Between GPUs 16 min
  18. Training at Scale: Internals 16 min
  19. Keeping It Alive at Scale 17 min
  20. Serving at Scale 17 min
  21. ML System Design 18 min

AI Security & Privacy

Build it safe, keep it private.

Securing and privatizing AI: data privacy, differential privacy, federated learning, adversarial ML, LLM security and prompt injection, privacy-preserving inference, secure deployment, guardrails and abuse prevention, data governance, and security architecture.

  • Advanced
  • 11 lessons
  • 168 screens
  • 3.5–5.5 h
Lessons (11)
  1. Data Privacy Foundations 17 min
  2. Differential Privacy 23 min
  3. Federated Learning 17 min
  4. Adversarial Machine Learning 18 min
  5. LLM Security: Prompt Injection and Jailbreaks 22 min
  6. Privacy-Preserving Inference 20 min
  7. Securing Deployed AI Systems 19 min
  8. Guardrails and Abuse Prevention 20 min
  9. Data Retention, Permissioning, and Governance 18 min
  10. AI Security Architecture 20 min
  11. AI Regulation and Governance 21 min

Systems Design & Engineering

The judgment AI cannot replace.

From systems thinking and requirements to the full modern stack: performance, scaling, databases, caching, distributed systems, APIs, the edge, messaging, resilience, reliability, observability, safe deploys, security, and data engineering for AI, with the tradeoff judgment AI cannot replace.

  • Advanced
  • 22 lessons
  • 341 screens
  • 6–9.5 h
Lessons (22)
  1. Thinking in Systems 16 min
  2. Requirements and Systems Engineering 17 min
  3. System Design Fundamentals 15 min
  4. Performance: Latency, Throughput, and Capacity 17 min
  5. Designing for Scale 17 min
  6. Caching, CDNs, and DNS 19 min
  7. Databases: SQL, NoSQL, and Storage Engines 20 min
  8. Scaling Data: Replication, Partitioning, and Sharding 17 min
  9. Distributed Systems 18 min
  10. APIs, Services, and Architecture 20 min
  11. API Styles: REST, GraphQL, gRPC and Serialization 17 min
  12. The Edge: API Gateway, Auth, and Rate Limiting 19 min
  13. Messaging and Event-Driven Architecture 15 min
  14. Reliability and SRE 17 min
  15. Resilience Patterns: Failing Gracefully 17 min
  16. Observability: Logs, Metrics, and Traces 16 min
  17. Shipping Safely: Deploys, Autoscaling, and Flags 17 min
  18. Security by Design 18 min
  19. Data Engineering for AI: Pipelines, Lakes, and Vector DBs 18 min
  20. Tradeoffs, Reviews, and Judgment 14 min
  21. System Design Interviews 16 min
  22. Final Exam: Building and Shipping AI 14 min

Stage 5 of 5

Robotics & Physical AI

Give intelligence a body: sense, plan, and act in the real world.

  • Beginner → Expert
  • 6 paths
  • 108 lessons
  • 2406 screens
  • 40–65 h

Electronics, From the First Spark

Wire it, measure it, make it work.

Electronics from nothing: charge, current and voltage, then resistors, capacitors, diodes and transistors, each one on a live circuit you can break and fix. Three real builds along the way, an LED, an Arduino and a motor driver, plus how a breadboard becomes a printed circuit board in KiCad. The foundation every robot stands on.

  • Beginner
  • 16 lessons
  • 286 screens
  • 4.5–7 h
Lessons (16)
  1. What Electricity Is 17 min
  2. Ohm's Law and Resistors 16 min
  3. Series, Parallel, and Kirchhoff 17 min
  4. Build 1: Light Your First LED 16 min
  5. Switches, Buttons, and Pots 15 min
  6. Measuring With a Multimeter 18 min
  7. Capacitors: Charge, Hold, Drain 18 min
  8. Diodes: One-Way Valves 17 min
  9. Transistors as Switches 18 min
  10. Amplify, Then Think: Logic From Transistors 19 min
  11. Power: Batteries and Regulators 18 min
  12. Signals: Analog, Digital, PWM, ADC 16 min
  13. Build 2: Power Up an Arduino 17 min
  14. Build 3: Spin a Motor Safely 17 min
  15. From Breadboard to PCB With KiCad 18 min
  16. Soldering and Finding the Fault 19 min

Physics for Robotics

The rules every robot obeys.

The mechanics under every machine: forces and Newton’s laws, motion, energy and power, rotation and torque, friction and contact, motors and electromagnetism, and the oscillation and damping that control loops must tame.

  • Intermediate
  • 8 lessons
  • 231 screens
  • 4–6.5 h
Lessons (8)
  1. Forces and Newton's Laws 32 min
  2. Motion: Velocity and Acceleration 32 min
  3. Energy, Work, and Power 32 min
  4. Rotation, Torque, and Momentum 33 min
  5. Friction, Contact, and Traction 30 min
  6. Electricity, Magnetism, and Motors 31 min
  7. Oscillation, Resonance, and Damping 28 min
  8. Electronics That Move a Robot 34 min

Robotics Foundations

Sense. Decide. Act.

Learn how robots sense, think, move, and interact with the world.

  • Intermediate
  • 11 lessons
  • 198 screens
  • 3–5 h
Lessons (11)
  1. What Makes a Robot a Robot? 16 min
  2. Sensors and Actuators 16 min
  3. Robot Brains 12 min
  4. Movement and Control 14 min
  5. Why Humanoid Robots Are Hard 15 min
  6. Coordinate Frames and Transforms 20 min
  7. Orientation: Rotations and Quaternions 32 min
  8. Where Am I? Localization 21 min
  9. Planning a Path 17 min
  10. How Robots See 18 min
  11. Build 4: Your First Rover 15 min

Physical AI & Robotics Engineering

Sense, model, plan, control, for real.

The deep robotics track: kinematics and dynamics, state estimation and SLAM, real-time perception and 3D vision, motion planning and control, manipulation and locomotion, and the frontier of physical AI: foundation models that turn perception straight into action.

  • Advanced
  • 24 lessons
  • 683 screens
  • 10.5–16 h
Lessons (24)
  1. Kinematics: Where the Arm Goes 30 min
  2. Inverse Kinematics and Jacobians 26 min
  3. Dynamics: Forces and Motion 25 min
  4. Control: From PID to MPC 31 min
  5. Impedance and Force Control 25 min
  6. State Estimation and the Kalman Filter 25 min
  7. Sensor Fusion 26 min
  8. SLAM: Mapping While Moving 28 min
  9. Real-Time Perception 22 min
  10. Depth, Point Clouds, and 3D Vision 27 min
  11. Motion Planning: A* to RRT* 27 min
  12. Manipulation and Grasping 23 min
  13. Legged Locomotion 26 min
  14. RL for Robots and Sim-to-Real 31 min
  15. Physical AI: Foundation Models for Robots 29 min
  16. Real-Time Systems, ROS 2, and the Edge 31 min
  17. Safety and Reliability in Physical Systems 33 min
  18. Sim-to-Real, Deeper 29 min
  19. Imitation Learning and the Data Engine 24 min
  20. Vision-Language-Action Models 25 min
  21. World Models and JEPA 24 min
  22. Spatial Intelligence 22 min
  23. Tactile Intelligence and the Force Bottleneck 23 min
  24. Cross-Embodiment and the Road Ahead 25 min

Robotics Systems & Integration

Turning parts into a working robot.

The real-world engineering: embedded systems, sensors and buses, ROS 2 integration, simulation, humanoids, hardware design, AI accelerators, voice and human-robot interaction, the supply chain and raw materials, and deploying a reliable fleet.

  • Advanced
  • 34 lessons
  • 799 screens
  • 15–23.5 h
Lessons (34)
  1. Embedded Systems and Microcontrollers 29 min
  2. Sensors, Devices, and Buses 27 min
  3. Talking to Motors 23 min
  4. Latency and the PID Loop 24 min
  5. Embedded Hardware, Hands-On 29 min
  6. Why Robots Run C++ 17 min
  7. Copies, References, and Pointers 21 min
  8. Memory and Who Owns It 19 min
  9. Reading Real Robot Code 16 min
  10. C++ and Real-Time Systems 39 min
  11. Integrating a Robot with ROS 2 46 min
  12. Robot Simulation and Digital Twins 41 min
  13. Humanoid Robotics 37 min
  14. Robot Hardware Design 42 min
  15. Voice UX and Human-Robot Interaction 39 min
  16. The Robotics Supply Chain 34 min
  17. Raw Materials and Manufacturing Hubs 36 min
  18. Product Reliability and Fleet Deployment 22 min
  19. The Compute Ecosystem 23 min
  20. AI Accelerators and Novel Hardware 42 min
  21. Edge Compute for Robots 20 min
  22. Same Model, Different Chip 22 min
  23. The Edge-Cloud Hybrid 19 min
  24. Event-Based Vision 20 min
  25. Power, Brains, and One Island 24 min
  26. The Hardwired Safety Tier 24 min
  27. Robots Among People 24 min
  28. Fleet Orchestration 25 min
  29. The Human in the Loop 22 min
  30. CapEx vs Robotics-as-a-Service 25 min
  31. Brownfield vs Greenfield 22 min
  32. Reverse Logistics and OTA Risk 24 min
  33. Black Box, Isolation, and Liability 23 min
  34. The Founder's Bet 27 min

Future Builder

See how it all connects.

Learn how AI, robotics, chips, and automation come together to create the future.

  • Intermediate
  • 15 lessons
  • 209 screens
  • 3.5–6 h
Lessons (15)
  1. The AI Stack 10 min
  2. Data Centers and GPUs 11 min
  3. Powering AI: The Power Wall 12 min
  4. Edge AI 13 min
  5. Robots in the Real World 10 min
  6. Building Your First AI Product 11 min
  7. Careers in AI and Robotics 21 min
  8. Safety, Alignment, and Policy 16 min
  9. Reading AI Research 17 min
  10. The Honest Limits of AI & Robotics 19 min
  11. Building a Career in the AI Age 19 min
  12. Build Your Project Portfolio 18 min
  13. Behavioral Interviews 17 min
  14. Your Resume and Job Search 18 min
  15. Final Exam: Robotics and Physical AI 15 min

Role tracks

Job-shaped routes through the same catalog, for people learning AI for their work rather than to become an engineer.

  • 8 paths
  • 78 lessons
  • 1104 screens
  • 19.5–30 h

AI at Work: Core

The foundation every role shares.

The six things every professional does whatever their job: pick the tool you already pay for, improve a prompt until it is usable, decide when NOT to use AI, automate the workflow you run every week, put an agent on real work, and break it.

  • Intermediate
  • 5 lessons
  • 64 screens
  • 75–115 min
Lessons (5)
  1. Improve the Prompt 15 min
  2. Choose When NOT to Use AI 14 min
  3. Automate the Workflow You Run Every Week 15 min
  4. Put an Agent on Real Work 16 min
  5. Break the Agent 15 min

AI for Finance & Professional Services

A week on the desk.

A week on the desk, in order: research a company, analyze the numbers, make the spreadsheet do the work, build the deck, read the documents, run the diligence pass, walk in prepared, catch your own invented number, and decide what leaves the building.

  • Intermediate
  • 10 lessons
  • 130 screens
  • 2.5–4 h
Lessons (10)
  1. Research a Company in Fifteen Minutes 15 min
  2. Analyze the Numbers You Were Handed 15 min
  3. Make the Spreadsheet Do the Work 14 min
  4. Build the Deck Before the Meeting 14 min
  5. Read 200 Pages You Do Not Have Time For 14 min
  6. Run the Diligence Pass 17 min
  7. Walk In Prepared 16 min
  8. Catch Your Own Invented Number 16 min
  9. Decide What Leaves the Building 15 min
  10. Prove It: A Week on the Desk 16 min

AI for Marketing

A week on the team.

A week on the team, in order: turn a vague brief into a working one, find out who you are talking to, make it sound like your brand, ship the campaign set, make the image and the video, get found in AI search, read what it actually did, and catch the claim you cannot back.

  • Intermediate
  • 10 lessons
  • 138 screens
  • 2–4 h
Lessons (10)
  1. Turn a Vague Brief Into a Working One 14 min
  2. Find Out Who You Are Talking To 13 min
  3. Make It Sound Like Your Brand 14 min
  4. Ship the Whole Campaign Set 13 min
  5. Make the Image and the Video 13 min
  6. Get Found When the Answer Is the Engine 13 min
  7. Read What the Campaign Actually Did 14 min
  8. Catch the Claim You Cannot Back 18 min
  9. Decide What You Are Allowed to Publish 15 min
  10. Prove It: A Week on the Team 14 min

AI for Sales

A week in the pipeline.

A week in the pipeline, in order: know the account before the call, write the message that gets a reply, run the discovery call, update the CRM without typing, write the proposal, prepare for the objection, commit a number you can defend, and check it before you say it.

  • Intermediate
  • 10 lessons
  • 143 screens
  • 2.5–4.5 h
Lessons (10)
  1. Know the Account Before the Call 17 min
  2. Write the Message That Gets a Reply 17 min
  3. Run the Discovery Call 19 min
  4. Update the CRM Without Typing 18 min
  5. Write the Proposal They Will Read 18 min
  6. Prepare for the Objection You Will Get 19 min
  7. Commit a Number You Can Defend 17 min
  8. Check It Before You Say It 16 min
  9. Decide Where Customer Data May Go 15 min
  10. Prove It: A Week in the Pipeline 21 min

AI for Every Role

An ordinary week.

An ordinary week, in order: clear the inbox without losing the one that mattered, turn meetings into owned actions, draft the document and the deck, ask the spreadsheet a question, produce a brief someone can check, run the project without the status meeting, and find the part that is not true.

  • Intermediate
  • 10 lessons
  • 123 screens
  • 2–3.5 h
Lessons (10)
  1. Clear the Inbox Without Losing the One That Mattered 14 min
  2. Turn the Meeting Into Owned Actions 14 min
  3. Get the Document and the Deck Drafted 14 min
  4. Read the Sixty Pages You Were Sent 13 min
  5. Ask the Spreadsheet a Question 15 min
  6. Produce a Brief Someone Can Check 13 min
  7. Run the Project Without the Status Meeting 12 min
  8. Find the Part That Is Not True 12 min
  9. Decide What You May Paste 13 min
  10. Prove It: An Ordinary Week 8 min

AI for Team and Business Leaders

A quarter of rollout.

The half of an AI rollout the four role tracks do not cover: find the work worth changing, price it honestly, run a pilot that could fail, write a policy people follow, buy without being sold to, teach one task at a time, review work you did not watch being made, and know what to do in the first hour after it goes wrong.

  • Intermediate
  • 10 lessons
  • 131 screens
  • 2–3.5 h
Lessons (10)
  1. Map the Week Your Team Actually Has 14 min
  2. Price the Hour Before You Promise the Saving 13 min
  3. Run the Pilot That Proves Something 14 min
  4. Write the Policy People Will Actually Follow 14 min
  5. Decide the Route for a New Tool 14 min
  6. Buy It Without Being Sold To 14 min
  7. Teach the Team, Not the Tool 14 min
  8. Review Work You Did Not Watch Being Made 14 min
  9. The First Hour After It Goes Wrong 14 min
  10. Prove It: A Quarter of Rollout 14 min

Earning With AI

Sell the result, not the tool.

The track for working on your own account: what you may legally sell, what you can honestly promise a client, how to get real work out of an agentic coding tool, how to check output you did not write, and how to scope and price a job so that getting faster does not cut your own income.

  • Intermediate
  • 10 lessons
  • 144 screens
  • 2.5–4 h
Lessons (10)
  1. Measure the Gain, Do Not Feel It 16 min
  2. Read the License Before You Quote 15 min
  3. What You Can Honestly Promise 15 min
  4. Get Real Work Out of a Coding Agent 15 min
  5. Review What You Did Not Write 15 min
  6. Generate Video a Client Will Accept 16 min
  7. Scope a Job You Can Deliver 16 min
  8. Price the Outcome, Not the Hour 14 min
  9. Find the First Client 17 min
  10. Prove It: Working On Your Own Account 19 min

AI for Business

Put AI to work in any company.

The practical playbook for using AI at work: the tools you can use today (assistants, image and video generators), how AI applies function by function across marketing, sales, support, finance, accounting, HR, operations, legal and every industry, plus how to roll it out safely with a human in the loop.

  • Beginner
  • 13 lessons
  • 231 screens
  • 3.5–5.5 h
Lessons (13)
  1. Where AI Actually Helps Your Business 18 min
  2. The Generative AI Toolbox (2026) 19 min
  3. AI Images and Video: Tools You Can Use Today 15 min
  4. AI for Marketing and Content 14 min
  5. AI for Sales 14 min
  6. AI for Customer Support 16 min
  7. AI for Finance and FP&A 18 min
  8. AI for Accounting 16 min
  9. AI for HR and Recruiting 15 min
  10. AI for Operations and Supply Chain 14 min
  11. AI for Legal and Compliance 17 min
  12. Industry Tour: Healthcare, Retail, Manufacturing and More 18 min
  13. Rolling Out AI in Your Organization 18 min

Start anywhere

Every path is open from the first lesson. The course adapts to what you already know, so you are not made to sit through what you can already do.

Start learning For teachers

Start with the first one.

AI Foundations is free, permanently, and it is where every path here begins.

Start learning free