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Create practical LLM applications with search, retrieval, RAG, evaluation, monitoring, and production workflows for AI systems.
Jun 8, 2026 - Aug 17, 2026 (10 weeks) GitHub
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Peer review: Project Attempt 1
Due 11 August 2026 (Tue), 21:00
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A hands-on path through machine learning engineering: regression, classification, trees, deep learning, serverless inference, Kubernetes, and a capstone project.
Sep 14, 2026 - Jan 25, 2027 (19 weeks) GitHub
Next assignment
Homework: Homework 1: Introduction to Machine Learning
Due 21 September 2026 (Mon), 23:00
Use AI coding tools in real development workflows: prompting, agents, testing, code review, and shipping production-quality software faster.
Aug 31, 2026 GitHub
Past cohorts remain open for self check; late submissions are not possible.
Use AI coding tools in real development workflows: prompting, agents, testing, code review, and shipping production-quality software faster.
A hands-on path through machine learning engineering: regression, classification, trees, deep learning, serverless inference, Kubernetes, and a capstone project.
Create practical LLM applications with search, retrieval, RAG, evaluation, monitoring, and production workflows for AI systems.
Analyze stock market data with Python, SQL, and modern data tools: data collection, indicators, dashboards, and trading-oriented analysis.
Productionize machine learning systems with experiment tracking, orchestration, deployment, monitoring, and reliable ML workflows.
Build production-style data pipelines with Docker, workflow orchestration, data warehouses, analytics engineering, batch processing, streaming, and a final project.
A hands-on path through machine learning engineering: regression, classification, trees, deep learning, serverless inference, Kubernetes, and a capstone project.
Create practical LLM applications with search, retrieval, RAG, evaluation, monitoring, and production workflows for AI systems.
Productionize machine learning systems with experiment tracking, orchestration, deployment, monitoring, and reliable ML workflows.
Analyze stock market data with Python, SQL, and modern data tools: data collection, indicators, dashboards, and trading-oriented analysis.
Build production-style data pipelines with Docker, workflow orchestration, data warehouses, analytics engineering, batch processing, streaming, and a final project.