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AI/ML

Statistical Reasoning and Reliable Evaluation
Python, SQL, and Coding Interview Patterns
Supervised Machine Learning from First Principles
Data-Centric and Advanced Classical ML
Deep Learning Foundations with PyTorch
Representation Learning, Attention, and Transformers
Applied LLM Engineering: RAG, Fine-Tuning, and Evaluation
ML Data Pipelines, Experimentation, and Monitoring
Serving and MLOps on AWS
Senior-Level ML and Applied AI System Design
Interview Integration and Portfolio Evidence