Learn machine learning by actually doing it
QuiddityML teaches the full ML stack with a clear roadmap, short lessons, 11 types of hands-on exercises, spaced repetition, interview questions, and portfolio projects, with the math taught where it comes up. Learn it once, then keep it.
- hands-on exercises
- 11,000+
- App Store rating
- 5.0 ★
Build understanding that holds
Not just watching and forgetting. QuiddityML takes you through structured tracks, hands-on coding exercises, and built-in spaced repetition that makes sure what you learn actually sticks.
Reading or watching videos is passive. QuiddityML makes you actually do the work. Progress through 4 tiers of difficulty, from recognizing concepts to implementing models from scratch, with live feedback on every answer.
QuiddityML tracks how well you know each concept and reminds you to review at exactly the right moment. No more forgetting what you learned last week.
Start from the basics, then work through real equations, code, diagrams, and model behavior deeply enough to read papers and build your own systems.
Learn it. Test it. Keep it
Each concept moves through the same loop: understand it, use it, then review it before it fades.
Each concept starts with slides: clear explanations, diagrams, equations, and worked code examples. Real depth, not bullet points. You read and understand before anything gets tested.
After each concept, exercises push you past basic recall. Spot bugs, fill in implementations, write code from scratch. You can't just skim your way through.
Spaced repetition schedules each concept based on how well you actually did, not just that you finished it. Struggled on an exercise? It comes back in a day. Nailed it? It waits weeks. Your review queue is built entirely from your own results.
Practice should look like the work itself
QuiddityML exercises are built around the things ML people actually do: recognize concepts, connect math to code, debug broken systems, and build models from scratch.
Recognize
Connect
Debug
scores = q @ k.transpose(-2, -1)
scores = scores / math.sqrt(d_ff)
weights = softmax(scores)
Build
Try real exercises, right here
These are real exercises from the app. 11,000+ more are waiting.
From fundamentals to the frontier
Start with math and ML foundations, then branch into NLP, retrieval, vision, RL, generative models, interpretability, safety, and many more areas of modern AI.
ML Foundation
Optimization, neural networks, backpropagation, and the core concepts behind modern ML.
Intro to NLP
Tokenization, embeddings, language models, and attention.
Retrieval & Search
Ranking, embeddings, and search-powered products.
RL Foundation
Rewards, value functions, policies, and exploration.
Mathematical Toolkit
Notation, logarithms, combinatorics, and Big-O.
Turn understanding into interview answers and project work
Every unit ends with questions tied to the material you just learned: concepts, coding, system design, and follow-ups.
Build small, real projects that prove you can apply the ideas outside the lesson.
What people say on the App Store
These are copied word for word from the App Store, where we cannot edit or remove them. Rated 5.0 across every review so far. Read them at the source: App Store reviews.
★★★★★The best app I've found!Such an amazing and helpful app! I wish everyone knew about it!
★★★★★This is the bomb diggityI've really been enjoying QuiddityML! The lessons are clear, interactive, and make complicated machine learning concepts much easier to understand. I especially like the hands-on exercises and the way the app encourages you to actually think through problems instead of just memorizing information
★★★★★Love it!I've been trying to get into machine learning for months, and there's just so much content and so many resources that it's overwhelming. Some have way too much theory, some don't have enough, courses are too long and outdated, and books are just boring. I honestly love this app. It makes learning ML actually fun, and the spaced repetition thing is brilliant. I'm surprised no one else did it before. The lessons are clear, visual, and broken into pieces that actually build on each other instead of dumping theory on you all at once. I also really like that it explains the "why" behind concepts, not just definitions, and has different exercises that build on each other and actually make you learn how to code these things. Also the axolotl is adorable
★★★★★Never seen anything like thisI've been looking for ways to customize an AI to execute specialized functions for my needs. I'm pretty sure this app just came out, but my goodness, this is exactly what I've been looking for. It teaches every step to building an AI. It literally goes through the basics to applying advanced concepts. The creator has been open and responsive to feedback, so the app is constantly being updated. Love this app and I can't believe I landed on it.
★★★★★Highly recommendedFun and easy way to learn machine learning. Very comprehensive.
QuiddityML vs books, YouTube, and courses
Every option below can teach you ML, and each is good at something. Here is what each one gives you, side by side.
| QuiddityML | BooksHands-On ML, ISLR | YouTubeStatQuest, 3Blue1Brown | MOOCsAndrew Ng on Coursera, fast.ai | Interactive sitesDataCamp, Brilliant, Kaggle Learn | |
|---|---|---|---|---|---|
| Spaced repetitionConcepts you're shaky on come back before they fade | ✓ | ✗ | ✗ | ✗ | Brilliant adaptive practice, DataCamp review practice, Kaggle no |
| 11 hands-on exercise typesWrite, debug and read real code and math, not just multiple choice | ✓ | end-of-chapter exercises and labs | ✗ | weekly quizzes and labs | coding, multiple choice, order the lines |
| Clear roadmapEvery concept in order, so you can see what's next and where your gaps are | ✓ | within one book | ✗ | within one specialization | DataCamp career tracks, others partial |
| Interview questions every unitPractice explaining concepts the way interviews ask | ✓ | ✗ | separate videos | separate courses | separate courses |
| A project at the end of every trackBuilt from concepts you've already practiced | ✓ | one project (Hands-On ML) | ✗ | fast.ai yes, Coursera labs | DataCamp guided, Kaggle on your own |
| Math in contextStart building sooner, and learn the math where it comes up | ✓ | explained, you build on your own | explains, no building | fast.ai yes, Andrew Ng optional videos | partial |
| Instant feedbackSee why an answer is wrong as soon as you submit it | ✓ | ✗ | ✗ | auto-graded quizzes and labs | ✓ |
| 5-minute lessonsEasy to fit into a busy day | ✓ | chapters | 10–30 min videos | hours a week | ✓ |
| Web, iPhone and MacProgress syncs across devices | ✓ | paper or ebook | watch only | quizzes on mobile, labs on a computer | DataCamp and Brilliant yes, Kaggle no |
| One app, 12 tracksPython and PyTorch through NLP and vision | ✓ | one topic per book | scattered | separate courses | partial |
Start free. Pay only if you want everything.
No card needed to start. Spaced repetition, the review queue, and progress tracking are included in both plans.
Enough to learn the foundations properly and decide if this is for you.
Everything, in any order, with no hearts to run out.
Become the person who understands it deeply enough to change it
Move from recognizing ML terms to reasoning through the math, debugging the code, explaining the tradeoffs, and building systems of your own.
