Train your
Neural intuition.
Go beyond `fit()` and `predict()`. Visualize gradient descent, backpropagation, and architecture patterns step-by-step.
class SelfAttention(nn.Module):
# Calculated scaled dot-product
def forward(self, x):
Q, K, V = self.split(x)
weights = Q @ K.T
weights = softmax(weights)
return weights @ V
Mathematics & Statistics
Linear Algebra, Probability, Calculus
Data Processing
Pandas, NumPy, Feature Engineering
Core Algorithms
Regression, SVM, Random Forests
Deep Learning
CNNs, RNNs, Transformers & LLMs
MLOps & System Design
Deployment, Monitoring, Scalability
From Math to Models.
Stop memorizing libraries. Understand the mathematics beneath the surface and build potential production-ready systems.
Our curriculum bridges the gap between theoretical research and practical engineering.
Explore CurriculumDon't reinvent the wheel.
Machine Learning has its own set of design patterns. Learn standard solutions to common problems like data scarcity, high cardinality, and model monitoring.
Embeddings
Represent categorical data as dense vectors to capture semantic meaning.
Transfer Learning
Fine-tune pre-trained models for downstream tasks with limited data.
Ensembling
Combine multiple weak learners to create a robust strong learner.
Build it from Scratch
True mastery comes when you can implement `K-Means`, `Logistic Regression`, or `Self-Attention` without any libraries. Our ML challenges test your ability to translate math into code.
class LogisticRegression:
def __init__(self, lr=0.01):
self.lr = lr
self.weights = None
self.bias = None
def fit(self, X, y):
# 1. Initialize parameters
n_samples, n_features = X.shape
self.weights = np.zeros(n_features)
# 2. Gradient Descent
for _ in range(self.iters):
linear_model = np.dot(X, self.weights) + self.bias
y_predicted = self._sigmoid(linear_model)
dw = (1 / n_samples) * np.dot(X.T, (y_predicted - y))
self.weights -= self.lr * dw