Pick one skill.
Drill it until it’s yours.
Challenges build a whole project; practice sharpens a single skill. Each one is ten bite-size tasks that climb from easy to advanced — real Python, real models, instant feedback, all in your browser.
Python
Ten reps that take you from variables and loops to comprehensions, generators, and decorators — the Python every ML engineer leans on daily.
NumPy
Vectors, matrices, broadcasting, and the math that powers every model. Stop writing loops — start thinking in arrays.
Pandas
Load, filter, group, and reshape tabular data — the daily grind of every data and ML project, drilled until it's muscle memory.
Data Visualization
Turn numbers into pictures with matplotlib — line, scatter, bar, histogram, and the touches that make a chart actually readable.
Regression
Fit lines and curves to data and measure the error — linear, polynomial, and regularised models on real datasets.
Classification
Tell classes apart — logistic regression, trees, KNN — and learn the metrics that tell you whether your model is actually any good.
Clustering
Group data with no labels at all — K-Means, the elbow method, and dimensionality reduction with PCA.
Feature Engineering
The work that moves the needle more than the model: encoding, scaling, binning, interactions, and leak-free pipelines.
Deep Learning
Build the network from first principles in NumPy — neurons, activations, forward pass, backprop, and gradient descent. The intuition every framework hides.
Want a skill that isn’t here yet?
Tell us what you want to practice next. We add skills and tasks all the time — your request helps decide what’s next.