Start at Lesson 1 and train forward in short, clear micro‑presentations. Machine learning looks like “magic” to most people — this course turns it into understandable rules.

1. Begin Lesson 1: Telling the Computer What We Want
2. Train the short slides (one idea at a time)
3. (Paid members) Take the drill quiz and record your score
4. Repeat daily — belt by belt

Goal: understand how ML learns from data, where it fails, and how it’s used to win (or cheat) in the real world.

AI is changing the job market fast. But AI isn’t magic — it’s mostly machine learning: systems that learn patterns from data and then make decisions at scale.

This course teaches the fundamentals of how ML works, what kinds of models exist, how they can fail (bias, overfitting, false conclusions), and how they are applied in real products.

Parent benefit: your teen won’t just “consume AI” — they’ll understand the weapon. And you get measurable progress through quizzes + belt exams (paid membership).

AI is going to outcompete average people at average work. Your move is to become the person who understands how AI works — and can build, test, and control it.

• Short missions (bite‑size training, not long lectures)
• Belts that prove you’re leveling up
• Retakes allowed (best score counts — improve without fear)
• Real power: understand trees, neural nets, Bayes, clustering, deep learning, language models

Challenge: reach Yellow Belt (your first real models) and show your score to a parent. If you like it, send it to a friend who wants to win too.

• What machine learning is (and how it differs from traditional programming)
• How to use Python notebooks / Google Colab at a basic level (conceptually)
• Major ML families: decision trees, neural networks, Bayes/Naive Bayes, genetic algorithms, nearest neighbors
• Overfitting and real-world pitfalls (bias, misinterpretation, spurious patterns)
• Core applications: clustering, recommendations, reinforcement learning, vision, language, speech
• Modern concerns: privacy, fraud potential, correlation vs causation
• Meta-learning: learning how to learn

• White Belt — Lessons 1–2: Fundamentals of learning + notebooks/Colab
• Yellow Belt — Lessons 3–6: First models (trees, nets, Bayes)
• Orange Belt — Lessons 7–8: More algorithms (genetic, neighbors)
• Green Belt — Lessons 9–10: Overfitting + real-world pitfalls
• Blue Belt — Lessons 11–12: Clustering + recommenders
• Brown Belt — Lessons 13–20: RL, deep learning, language, GANs, speech
• Black Belt — Lessons 21–25: IRL, causality, privacy, meta-learning

Free (Guest Training)

• Access the free lesson content (training slides / micro‑presentations)
• Read the curriculum and belt map
• Try sample drills (optional)

Dojo Membership (Paid)

• Full access to all drills, quizzes, and belt tests
• Belt tracking and certificates
• Parent progress tracking (scores + activity history)

Founders / Inauguration Price: $5 per course for 30 days (about the price of a coffee). This is the launch price while the dojo is expanding — as more belts, exams, and courses are added, the price will rise.

1. Telling the Computer What We Want
2. Starting with Python Notebooks and Colab
3. Decision Trees for Logical Rules
4. Neural Networks for Perceptual Rules
5. Opening the Black Box of a Neural Network
6. Bayesian Models for Probability Prediction
7. Genetic Algorithms for Evolved Rules
8. Nearest Neighbors for Using Similarity
9. The Fundamental Pitfall of Overfitting
10. Pitfalls in Applying Machine Learning
11. Clustering and Semi-Supervised Learning
12. Recommendations with Three Types of Learning
13. Games with Reinforcement Learning
14. Deep Learning for Computer Vision
15. Getting a Deep Learner Back on Track
16. Text Categorization with Words as Vectors
17. Deep Networks That Output Language
18. Making Stylistic Images with Deep Networks
19. Making Photorealistic Images with GANs
20. Deep Learning for Speech Recognition
21. Inverse Reinforcement Learning from People
22. Causal Inference Comes to Machine Learning
23. The Unexpected Power of Over-Parameterization
24. Protecting Privacy within Machine Learning
25. Mastering the Machine Learning Process

Disclaimer: This course provides education and training and cannot guarantee a specific job outcome.

Passing score: 80%
Retries: Unlimited
Score policy: Best score counts

How belts are earned

1. Training Drills (Lesson Quizzes) — short quizzes after lessons
2. Belt Test (Rank Exam) — a larger exam covering the belt section

Eligibility

You must complete the drills for the lessons in that belt section to unlock the belt test.

Integrity (important)

• This is You vs AI: no AI tools or outside help during belt tests.
• Drills are for learning; belt tests are for proof.
• Parents are encouraged to be present or nearby during belt tests.

Disclaimer: Belts and certificates recognize course progress and assessment performance. They do not guarantee a job outcome.