Machine Learning Fundamentals — Master core ML concepts and algorithms including regression, classification, clustering, and dimensionality reduction.
Deep Neural Networks — Explore advanced neural network architectures including CNNs, RNNs, transformers, and attention mechanisms.
Computer Vision — Unlock the power of visual data analysis with image classification, object detection, and segmentation problems.
Natural Language Processing — Harness the potential of language in AI with tokenization, embeddings, and sequence model challenges.
Why Deep-ML?
Constantly updating problems — New ML challenges are added all the time.
Problems made by professionals — Machine learning scientists and engineers create all problems.
Choice of learning — Learn Linear Algebra, Machine Learning, Deep Learning, NLP, and Computer Vision.
Free to use — Practice as much as you want, all problems are free.
Community Support — Connect with peers and mentors for guidance and collaboration.
Designed for career growth — Problems designed to help you prepare for ML interviews and grow in your career.
How Deep-ML Works
Deep-ML is a free machine learning practice platform. Browse ML coding challenges by difficulty and category. Write Python solutions in the browser-based code editor. Get instant feedback against test cases. Track your progress across collections and earn badges. Work with real datasets in labs. Learn through interactive articles with math visualizations. Discuss with the community: share machine learning interview experiences, ask ML questions, and debrief contests in the forum, where any referenced problem links straight to solving it.
Implementmachinelearningfromscratch.Implement machine learning from scratch.
Every problem here is one you implement yourself. No library call standing in for the thing you are trying to learn. Real Python, real compute, free.
An ordered route through the problems, labs and projects, built in the order they make sense. Each path is bound as a book, and beginner through advanced is inside it rather than split across three separate courses.
Meet Zero. Ask him anything and he answers from the real catalogue: he recommends actual problems, builds a playlist you save with one click, and remembers what you are working on. On a problem page he reads the question and your editor code, so he is tutoring you on the thing in front of you.
Implement an algorithm and test it on an actual dataset, with constraints, evaluation metrics and time limits. No CUDA install, no environment to break.