12 weeks · 5 modules · 12 portfolio projects
A practical AI/ML course for molecular biology scientists who want to lead at the intersection — not just spectate from the bench.
Complete each module to unlock the next. Each builds on what you learned before. Every module has projects you'll push to GitHub.
Master Claude Code CLI, prompt engineering, agents, MCP servers, automation, and the vibe coding workflow that powers the rest of this course.
Upgrade your Python from lab scripts to structured data science. EDA, statistics for ML, classification, regression — all with biological data.
Embeddings, foundation models (ESM-2, Nucleotide Transformer), transcriptomics ML, deep learning concepts, and cell image analysis.
Streamlit dashboards, LLM-powered tools, data apps, API integration — build things other scientists can actually use.
Data strategy for ML, capstone pipeline, portfolio polish, blog post, and positioning yourself at the intersection.
You run assays, analyze sequences, culture cells. You have deep domain knowledge in molecular biology, immunology, or therapeutics.
You've written analysis scripts, maybe automated some data processing. You're not a software engineer, but you're not afraid of code.
You want to work at the intersection of biology and AI — leading projects, making data strategy decisions, and eventually building companies.
You use Claude, Cursor, or Copilot to write code. This course leans into that — every project is designed to be vibe coded with AI tools.
ML concepts mapped to lab language you already think in.