MACHINE LEARNING

From math to models that matter.

Master the complete ML stack—from linear algebra and probability to neural networks, transformers, and reinforcement learning. Build intuition through 300+ hands-on problems.

Start ML JourneyPractice ML Problems
300+
ML Problems
19
Chapters
50+
Topics
neural_network.py
Training Progress
Epoch 47/100
Converging
Input
Hidden
Hidden
Output
Loss
0.0234 ↓
Accuracy
97.8% ↑
LR
0.001 —
COMPREHENSIVE CURRICULUM

19 Chapters. 316+ Problems.

From mathematical foundations to cutting-edge deep learning. Master every concept with hands-on practice.

Ch. 1

Linear Algebra Fundamentals

46 lessons
Ch. 2

Probability & Statistics

46 lessons
Ch. 3

Data Preprocessing & Feature Engineering

35 lessons
Ch. 4

Calculus & Optimization

28 lessons
Ch. 5

Classical ML Algorithms

13 lessons
Ch. 6

Evaluation Metrics

28 lessons
Ch. 7

Neural Network Fundamentals

25 lessons
Ch. 8

Optimization & Training

20 lessons
Start Learning
PATTERN-BASED LEARNING

Learn the patterns.
Master the concepts.

ML interviews test your understanding of core patterns—gradient descent, backpropagation, attention mechanisms, and more. Our pattern-based track helps you recognize and apply these patterns across different problem domains.

Gradient Descent

Optimizing loss functions

Backpropagation

Computing gradients in networks

Self-Attention

Transformer architecture core

Explore ML Patterns
ML Pattern Explorer
LOSS OPTIMIZATION
θ = θ - α∇J(θ)
Following the gradient to minimize loss
# Gradient Descent Update
weights = weights - learning_rate * gradients
Medium

Implement Gradient Descent

Implement gradient descent optimization for linear regression with the given learning rate and iterations.

Function: gradient_descent(X, y, lr, epochs)
# Your solution
def gradient_descent(X, y, lr, epochs):
# Initialize weights
w = np.zeros(X.shape[1])
for _ in range(epochs):
# Compute gradient
grad = X.T @ (X @ w - y) / len(y)
w = w - lr * grad
return w
All test cases passed
Runtime: 12ms

ML Problem Solving

Practice implementing ML algorithms from scratch. From matrix operations to transformer architectures, build the intuition that makes you interview-ready.

Linear Algebra
46+ problemsFoundation
Neural Networks
25+ problemsCore
Deep Learning
30+ problemsAdvanced
Transformers & LLMs
14+ problemsExpert
Practice ML ProblemsAll Problems
Join the AI revolution

Ready to master
machine learning?

From mathematical foundations to state-of-the-art models. Build the skills that power the future of technology.

Start Learning MLPractice Problems
ML Patterns Track →

OneNoughtOne