LLM102 · Python Essentials for AI Applications

Categories: AI & Automation
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About Course

The Python you actually need to build AI applications: data types, functions, files, JSON, HTTP requests, virtual environments and safe handling of API keys.

Explore · 4 hours · 4 lessons, a lab, an assessed assignment and a portfolio piece

Who it is for

Beginners and professionals from other languages who want a fast, practical Python foundation for LLM work.

Prerequisites

LLM101 is recommended; no prior Python experience is required.

What you will be able to do

  • Write Python functions that clean and transform text and lists of records
  • Read and write JSON and CSV files that represent business data
  • Send an HTTP request to a public JSON API and handle errors and timeouts
  • Set up a project with a virtual environment, a requirements file and environment variables for secrets

Lessons

  • Lesson 1 · Python building blocks for text and data
  • Lesson 2 · Files, JSON and HTTP
  • Lesson 3 · Projects, environments and secrets
  • Lesson 4 · Lab and assignment

Lab: A small text-processing toolkit

You will build a small Python module that loads customer messages from a CSV file, cleans the text, groups messages by topic using simple keyword rules, and saves the result as JSON. You will then fetch data from a free public JSON API to practice HTTP requests and error handling.

Free tools: Google Colab (free) or VS Code with Python 3.12 on your computer, plus a free GitHub account

Assessed assignment

Extend your toolkit into a script that a small online store could run every morning. It should read the day's messages from a CSV, clean and tag them, count messages per tag, and write both a JSON file and a one-paragraph text summary. Add at least four small tests using plain <code>assert</code> statements or <code>pytest</code>. Document how to install and run it in a short README section of your repository.

Criterion Weight What excellent work looks like
Correctness 35% The script runs from a clean setup, processes the sample file correctly and produces valid JSON.
Code quality 25% Functions are small and well named, there is no duplicated logic, and errors are handled with clear messages.
Tests 20% At least four meaningful tests that cover normal cases and at least one edge case such as an empty message.
Project hygiene 20% Virtual environment, requirements file, .gitignore and setup instructions are present; no secrets are committed.

Portfolio piece

A public GitHub repository with a tested Python text-processing script and clear setup instructions.

Self-paced, 12-month access. Questions are answered in writing through the student panel. A completion record is issued when you finish the course.

Next step

Continue with LLM103 · Working with LLM APIs · 6 hours · $42.00

Continue to the next course

Prefer the whole program? Program in AI Application Development & LLM Engineering · $378.00 (10% less than buying the courses separately)

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What Will You Learn?

  • Write Python functions that clean and transform text and lists of records
  • Read and write JSON and CSV files that represent business data
  • Send an HTTP request to a public JSON API and handle errors and timeouts
  • Set up a project with a virtual environment, a requirements file and environment variables for secrets

Course Content

LLM102 · Python Essentials for AI Applications (4 h)
The Python you actually need to build AI applications: data types, functions, files, JSON, HTTP requests, virtual environments and safe handling of API keys.

  • Lesson 1 · Python building blocks for text and data
  • Lesson 2 · Files, JSON and HTTP
  • Lesson 3 · Projects, environments and secrets
  • Lesson 4 · Lab and assignment
  • LLM102 Assessment · Python Essentials for AI Applications
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