Deploy, monitor, and scale ML systems in production with Docker, Kubernetes, CI/CD, and experiment tracking.
Master Python fundamentals and essential libraries for ML including NumPy, pandas, and scikit-learn.
Supervised and unsupervised learning, model evaluation, feature engineering, and real-world ML pipelines.
Deploy, monitor, and maintain ML systems. Docker, Kubernetes, CI/CD, experiment tracking, and drift detection.
Design scalable ML systems: recommendation engines, search ranking, fraud detection, and real-time serving.