15 days · 1 hour/day · Real projects · Industry workflow
Python, VS Code, venv, pip — and Git before your first model
Vectorized math, broadcasting, shapes — images are just numbers
DataFrames, CSV, groupby — understand your data before you model it
matplotlib + seaborn — a chart that tells the story instantly
scikit-learn, train/test split, linear regression — predict house prices
Decision Tree, Random Forest, confusion matrix — build a spam filter
Perceptron, layers, activation — teach a machine to recognise digits
Tokenization, TF-IDF, sentiment analysis — read between the lines
Groq API, system prompts, temperature — talk to an LLM like an engineer
Chains, prompts, output parsers — build a Q&A bot in 30 lines
FAISS, embeddings, retrieval-augmented generation — give the LLM your documents
Tool use, ReAct loop — an agent that searches, calculates & writes code
Serve your ML model as a REST API — from notebook to production endpoint
Railway, env vars, Docker basics — your AI app live on the internet
Full AI pipeline + pytest + git-flow — ship like a real AI engineer
15 real-world AI project ideas — from beginner ML to advanced RAG agents — to grow your portfolio.
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