From bias-variance fundamentals to PyTorch neural networks and production MLOps. Master supervised learning, clustering, dimensionality reduction, and deploy models that survive real-world data drift.
1-month free Pro trial included
Supervised vs unsupervised, bias-variance tradeoff, the learning process, and how gradient descent actually works
Linear and logistic regression from math to code, decision trees, random forests, gradient boosting, and SVM
Perceptrons to deep networks, backpropagation intuition, CNNs for images, and training with PyTorch
K-means, DBSCAN, PCA, t-SNE for dimensionality reduction, and rigorous model evaluation with cross-validation and learning curves
Feature stores, model versioning with MLflow, serving via FastAPI, monitoring for drift, and the full MLOps lifecycle
