Advanced ML: Unsupervised Learning & Production

Master advanced machine learning techniques including unsupervised learning, neural networks fundamentals, and production deployment. Build production-ready ML systems with modern MLOps practices.

Learning Objectives

• Master unsupervised learning: clustering, dimensionality reduction, anomaly detection
• Understand neural network fundamentals and deep learning basics
• Learn time series analysis and reinforcement learning intro
• Deploy ML models to production with proper infrastructure
• Implement MLOps best practices: monitoring, versioning, pipelines
• Optimize models for production: quantization, pruning, distillation
• Handle data drift, A/B testing, and model debugging
• Build scalable ML serving infrastructure

Course Content