Week 2: Python Essentials and My First AI/ML Concepts

Week 2 done. This week wasn't about fancy ML models or neural networks. It was about something more fundamental: building the Python muscle I'll need to debug AI code, even when Claude Code writes most of it. New here? Read Week 1: Why I'm making this transition first. What I Actually Did This week I focused on the tools, not the theory: Python fundamentals: NumPy for numerical operations Pandas for data manipulation Matplotlib for visualizations Jupyter notebooks as my workspace AI/ML concepts I started exploring: Embeddings (turning text into numbers) Prompt engineering basics Tool calling with LLMs Basic LLM API calls through notebooks Not glamorous. But necessary. The Realization: I Need to Read AI Code, Not Just Generate It Here's what hit me this week. I've been using Claude Code to build POCs 2-3x faster. That's great. But when something breaks? When the generated code doesn't do what I expect? When I need to understand WHY it works? I need to read Python. Not just generate it.