Practice

Core probability, distributions, estimation, and statistical inference.

Cross-cutting concepts that apply across supervised and unsupervised learning.

Prediction with labeled data: linear models, trees, SVMs, and more.

Clustering, dimensionality reduction, and anomaly detection.

Validation, metrics, and A/B testing for ML systems.

Optimization methods and hyperparameter tuning for ML.

Neural networks, regularization, and high-level architectures.

Linear algebra and calculus essentials for ML.