Archive
Structured guides and deep dives across Python, AI/ML, automation, and modern infrastructure. From first principles to production.
Claude AI
How to use Claude as a full-stack data scientist - automating CSV cleanup, PDF extraction, web research, report generation, and scientific analysis without writing every line of code yourself.
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Python for AI & Data Science
Build working intuition for linear and logistic regression in scikit-learn. Learn to fit, evaluate, and interpret both algorithms with hands-on code, proper metrics, and regularization techniques.
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Claude Code
Your CTO just asked: "How long is our code review cycle taking? Who's bottlenecking? Are we meeting SLAs?"
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MLOps & Infrastructure
You're managing a growing ML team. Models keep changing.
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AI & Machine Learning
Master the foundational thinking behind machine learning -- from problem framing and the bias-variance tradeoff to the scikit-learn API. Learn to ask the right questions before writing a single line of model code.
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MLOps & Infrastructure
You've trained a shiny new recommendation model that beats the old one by 3% on your held-out validation set.
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Claude AI
System prompts, few-shot examples, chain-of-thought, and prompt chaining are not interchangeable - knowing which technique matches which problem type is what separates production-ready prompts from ones that just work sometimes.
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MLOps & Infrastructure
Pull together everything from the Data Science cluster in this capstone project. Build a production-ready data pipeline that ingests, validates, cleans, transforms, and exports data from multiple sources.
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Claude Code
You've probably been there: a feature ships, users love it, and then—boom—your dashboard lights up red. Response times are creeping into the danger zone. Database queries are piling up.
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MLOps & Infrastructure
You've built an amazing ML model. Your validation metrics look great.
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