COURSE 21Intermediate8 IN-DEPTH LESSONS

Coding Labs (Python)

Hands-on Python implementations: building mini-RAG systems, image classifiers, text embeddings pipelines, and bounded agent execution loops.

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Course Syllabus & Units

Unit 1

Setup

1 lessons
1.1L1

Python lab setup

A clean **Python lab setup** so you can run ANN Learn code labs safely: editor, virtual environment, packages, and secrets hygiene.

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Unit 2

Ml

1 lessons
2.1L1

Tiny classifier lab

A hands-on lab: train a **tiny text or tabular classifier** so training/val/test and metrics become concrete.

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Unit 3

Apis

1 lessons
3.1L1

Safe API calls lab

A lab for calling hosted model APIs without leaking secrets, logging PII, or ignoring rate limits and costs.

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Unit 4

Rag

1 lessons
4.1L1

Mini RAG lab

A laptop-scale lab: chunk a few docs, embed, retrieve, generate with citations—wiring Course 09 ideas into code.

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Unit 5

Agents

1 lessons
5.1L1

Mini agent loop lab

A small lab implementing plan → tool → observe → answer with hard permission gates and a max-step budget.

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Unit 6

Eval

1 lessons
6.1L1

Eval script lab

A lab to write a tiny offline eval runner: load fixtures → call model/system → score → print a report—and fail CI on regressions.

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Unit 7

Demo

1 lessons
7.1L1

Packaging a demo lab

Turning a notebook into a **shareable demo**: README, env example, pinned deps, sample data, and clear limitations—without leaking secrets.

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Unit 8

Tensors

1 lessons
8.1L1

Tensors intuition lab

A gentle lab for **tensors** as multi-dimensional arrays—shapes, batching, and why frameworks care—without requiring GPU expertise.

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