Pick a track.
Commit to the reps.
Every challenge runs entirely in your browser — the brief on the left, a real editor and live output on the right, on a focus timer. Real datasets, real scikit-learn, real AI models. Finish a track and walk away with a portfolio that proves it.
7 Days of ML
Load data, train models, and ship a real classifier — one focused build a day for a week.
7 Days of AI
From prompts to a working AI feature you can actually demo — one build a day.
7 Days of MLOps
Package, deploy, and monitor a model end to end, one step a day.
Noob Dev Engineer
A sealed, timed exam: 20 intermediate-to-advanced questions across ML and AI, 10 minutes each. Pass to earn the Noob Dev Engineer badge.
Beginner
Ten gentle challenges — 5 machine learning, 5 AI — to get the core loop under your fingers.
Intermediate
Ten challenges — 5 ML, 5 AI — on the habits that separate a demo from a real model.
Advanced
Ten harder challenges — 5 ML, 5 AI — on the problems real datasets and real models actually throw.
30 Days of Data Analytics
From spreadsheets to SQL to dashboards — 30 days of turning raw data into decisions a business can act on.
30 Days of Data Science
The full pipeline — wrangling, modelling, evaluation, and communicating results — across 30 hands-on days.
30 Days of Data Engineering
Build the pipelines everything else depends on — ingestion, warehousing, and orchestration over 30 days.
30 Days of SQL
From SELECT to window functions and query tuning — the language every data role runs on, one day at a time.
30 Days of Python for Data
NumPy, pandas, and clean, reusable code — the Python foundation every analyst and scientist needs.
30 Days of Statistics
Probability, inference, and hypothesis testing — the intuition that keeps your conclusions honest.
30 Days of Deep Learning
Build up from a single neuron to full networks — forward passes, backprop, and training that converges.
30 Days of NLP
From tokens and TF-IDF to transformers — the techniques that turn raw text into understanding.
30 Days of Computer Vision
Images as data — filters, features, and convolutional networks for classification and detection.
30 Days of Generative AI
Text, images, and beyond — how generative models work and how to build real products on them.
30 Days of LLM Engineering
Beyond the prompt — RAG, evaluation, caching, and cost control for LLM systems that hold up in production.
30 Days of AI Agents
Give models hands — planning, tool use, memory, and multi-step agents that actually get work done.
30 Days of Prompt Engineering
A daily drill in the patterns that reliably steer LLMs — few-shot, chain-of-thought, structure, and evals.
30 Days of MLOps in Production
The full lifecycle — packaging, CI/CD, monitoring, and retraining — for models that survive contact with prod.
30 Days of Time Series
Trends, seasonality, and forecasting — from classic models to ML approaches for data that moves through time.
30 Days of Recommender Systems
The engines behind “you might also like” — collaborative filtering, embeddings, and ranking, day by day.
30 Days of Reinforcement Learning
Agents that learn from consequences — rewards, policies, and Q-learning, built up over 30 days.
30 Days of Big Data
When data won’t fit in memory — distributed processing with Spark and the patterns of big-data pipelines.
30 Days of Business Intelligence
Metrics, modelling, and visualization — turn warehouse data into dashboards leaders actually use.
30 Days of AI & ML Security
Adversarial attacks, prompt injection, and model hardening — securing AI systems end to end.
Want a lab that isn’t here yet?
Tell us what you want to learn next, or just say hi. We ship new labs and tracks all the time — your request helps decide what’s next.