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Analyze stock market data with Python, SQL, and modern data tools: data collection, indicators, dashboards, and trading-oriented analysis.
Aug 24, 2026 GitHub
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Project: Project Attempt 1
Due 20 November 2026 (Fri), 23:59
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
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Project: Midterm Project
Due 16 November 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
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Project: Project 2
Due 16 November 2026 (Mon), 23:00
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A free, project-based course on building production AI systems in which agents orchestrate GPU-powered image, video and audio models. Pre-Course Live Q&A: 10 Nov 2026; starts Mon 16 Nov; teaching ends Sun 20 Dec; first capstone submission 15 Jan 2027; final peer review 5 Feb 2027.
Nov 16, 2026 - Dec 20, 2026 (5 weeks) GitHub
Next assignment
Homework: Module 1: Make it work
Due 30 November 2026 (Mon), 23:59
Past cohorts remain open for self check; late submissions are not possible.
Create practical LLM applications with search, retrieval, RAG, evaluation, monitoring, and production workflows for AI systems.
Build production-style data pipelines with Docker, workflow orchestration, data warehouses, analytics engineering, batch processing, streaming, and a final project.
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.