AI-Powered ML Education

Learn Machine Learning by Building It

Compact, interactive coding problems that teach real ML concepts. Write code, get feedback, track your mastery — all in the browser.

gradient_descent.py

Gradient Descent Update

Implement one gradient descent step. Update each parameter by subtracting the gradient scaled by the learning rate.

θ_new = θ - α · ∇L

def gradient_step(params, grads, lr):

"""Apply one GD step."""

# YOUR CODE HERE

return params - lr * grads

✓ 3/3 tests passed

Everything you need to master ML

Built for engineers who learn by doing

Write Python in a full Monaco editor with syntax highlighting, autocomplete, and instant feedback — no setup required.

Stuck? Get progressive hints from Level 1 (conceptual nudge) to Level 3 (full explanation). The AI diagnoses your specific mistake.

Every concept has an endless supply of validated problems generated by AI and audited for quality. Never run out of practice.

Bayesian mastery tracking adjusts difficulty and unlocks concepts when you're ready — not on a fixed schedule.

From NumPy to neural networks — a structured path to ML mastery.

NumPy Arrays

Foundations

Gradient Descent

Classical ML

Backpropagation

Deep Learning

Regularization

Deep Learning

Adam Optimizer

Deep Learning

Model Evaluation

Classical ML

Join learners building real intuition for machine learning through hands-on coding problems.