🐍 python-for-ai102

Learn only the Python you need for Microsoft AI-102 labs — 6 Jupyter Notebooks covering foundations to Azure SDK patterns. Free & open-source.

📌 Purpose

Microsoft's AI-102: Designing and Implementing a Microsoft Azure AI Solution labs are now Python-only. Students who are new to Python often struggle to follow the lab code — not because of the AI concepts, but because they can't read the Python.

This course bridges that gap. It teaches only the Python you need to confidently read, understand, and modify code in AI-102 lab exercises. No fluff — every example connects directly to patterns found in the official Microsoft Learning GitHub repositories.

┌─────────────────────────────────────────────────────────────────┐
│ │
│ Student with This Course Ready for │
│ no Python ───────────────────────► AI-102 Labs │
│ experience (6 hours) │
│ │
│ ┌─────────────────────┐ │
│ │ Python Basics │ │
│ │ Data Structures │ │
│ │ Functions & Modules│ │
│ │ Files & Config │ │
│ │ REST APIs │ │
│ │ Azure AI Patterns │ │
│ └─────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────┘

🎯 Who Is This For?

Audience	Benefit
🎓 Students new to Python	Learn Python through AI-102 relevant examples
🧑‍🏫 MCT / Trainers	Ready-made 6-hour pre-course module
💼 IT Pros switching to AI	Quick ramp-up before certification prep
🔄 C# developers	Understand Python equivalents for AI-102 labs

📂 Repository Contents

python-for-ai102/
│
├── 📓 01_Python_Foundations.ipynb ← Notebook 1
├── 📓 02_Data_Structures.ipynb ← Notebook 2
├── 📓 03_Functions_Modules_ErrorHandling.ipynb ← Notebook 3
├── 📓 04_Files_JSON_DotEnv.ipynb ← Notebook 4
├── 📓 05_REST_APIs.ipynb ← Notebook 5
├── 📓 06_Complete_AI102_Pattern.ipynb ← Notebook 6
│
├── 📄 requirements.txt ← All Python packages needed
├── 🔐 .env.example ← Sample environment variables template
└── 📄 README.md ← You are here

🗺️ Course Outline — Notebook Details

📓 Notebook 1: Python Foundations (~1 hour)

"Before you can talk to Azure AI, you need to speak Python."

Topic	AI-102 Relevance
`print()` function	Display API responses, debug output
Variables & assignment	Store endpoints, API keys, results
Data types (`str`, `int`, `float`, `bool`)	Understand what kind of data you're working with
String operations	URL building, text manipulation
f-strings (formatted strings)	Used in every single AI-102 lab
`input()`	Interactive labs that take user text
Basic arithmetic	Confidence scores, percentages
Comments	Reading lab code annotations

📓 Notebook 2: Data Structures (~1 hour)

"Azure AI responses are lists of dictionaries. Master these two, and you can parse anything."

Topic	AI-102 Relevance
Lists `[]`	Collections of entities, phrases, objects
Indexing & slicing	Access specific results
`for` loops	Process each item in a response
`enumerate()`	Loop with position tracking
Dictionaries `{}`	JSON responses map directly to dicts
`.get()` safe access	Avoid crashes on missing keys
Nested structures	Navigate real API response trees
List comprehensions	Filter results concisely

📓 Notebook 3: Functions, Modules & Error Handling (~1 hour)

"AI-102 labs organize code into functions — and always wrap API calls in try/except."

Topic	AI-102 Relevance
Defining functions (`def`)	Every lab has functions like `analyze_text()`
Parameters & return values	Pass text in, get results back
Default parameters	Optional settings like `language="en"`
`if/elif/else`	Handle different sentiments, check thresholds
`while` loops	Continuous input loops in labs
`import` statements	Load Azure SDKs and utilities
`os` module	Environment variables, file paths
`json` module	Parse API responses
`try/except/finally`	Graceful error handling

📓 Notebook 4: Files, JSON & .env Configuration (~1 hour)

"Every AI-102 lab starts by loading a .env file. Every response is JSON. Every Vision lab reads an image file."

Topic	AI-102 Relevance
Reading text files	Analyze reviews, documents
Writing files	Save results
`.env` file pattern	Store API keys securely
`load_dotenv()` + `os.getenv()`	The first 3 lines of every lab
`json.load()` / `json.dump()`	Read/write JSON config & results
`json.loads()` / `json.dumps()`	Parse API response strings
Binary file reading (`"rb"`)	Send images to Vision API
`os.listdir()` + `os.path.join()`	Batch-process files in a folder

📓 Notebook 5: REST APIs & HTTP Requests (~1 hour)

"At the heart of every Azure AI service is a REST API. Learn the request-response cycle."

Topic	AI-102 Relevance
HTTP methods (GET, POST)	POST data for analysis, GET results
Request anatomy (URL, headers, body)	Build complete API requests
`Ocp-Apim-Subscription-Key` header	Azure API authentication
`Content-Type` headers	JSON vs binary (images)
`response.status_code`	Check for success or errors
`response.json()`	Parse the result
HTTP status codes	Debug 401, 403, 429 errors
Query parameters	API version, feature selection
Sending binary image data	Vision API pattern

📓 Notebook 6: The Complete AI-102 Pattern (~1 hour)

"All 5 notebooks come together. This is what real AI-102 lab code looks like."

Topic	AI-102 Relevance
REST API client pattern	Language Detection lab structure
SDK client pattern	Text Analytics with `TextAnalyticsClient`
Computer Vision pattern	Image analysis with caption, tags, objects
Batch file processing	Analyze all files in a folder
Quick reference cheat sheet	Copy-paste patterns for labs
`pip install` reference table	Every Azure SDK package you'll need

💻 System Requirements

Hardware

Component	Minimum	Recommended
RAM	4 GB	8 GB
Disk Space	2 GB free	5 GB free
Internet	Required for AI-102 labs	Required for AI-102 labs

Note: The 6 training notebooks themselves work offline — no Azure subscription or internet needed. Internet and Azure credentials are only needed when you move to actual AI-102 lab exercises.

Operating System

OS	Supported
Windows 10/11	✅ Yes
macOS 12+	✅ Yes
Ubuntu 20.04+ / Linux	✅ Yes

🛠️ Software Requirements

Required Software

Software	Version	Purpose	Download
Python	3.9 or higher	Run all notebooks and labs	python.org/downloads
Jupyter Notebook or JupyterLab	Latest	Run `.ipynb` files interactively	Installed via `pip` (see below)
VS Code(recommended)	Latest	Code editor with Jupyter support	code.visualstudio.com

VS Code Extensions (Recommended)

Extension	Publisher	Purpose
Python	Microsoft	Python language support
Jupyter	Microsoft	Run notebooks inside VS Code
Pylance	Microsoft	IntelliSense for Python

For AI-102 Labs (After This Course)

Software	Purpose
Azure CLI	Manage Azure resources from terminal
Git	Clone Microsoft Learning lab repos
Azure Subscription	Deploy and use AI services

🚀 Setup & Installation

Step 1 — Install Python

Download and install Python 3.9+ from python.org.

⚠️Windows users: Check the box "Add Python to PATH" during installation.

Verify installation:

python --version

Step 2 — Clone or Download This Repository

# Option A: Using Git
git clone https://github.com/<your-org>/python-for-ai102.git
cd python-for-ai102
# Option B: Download ZIP and extract from GitHub# Then navigate to the extracted folder

Step 3 — Create a Virtual Environment (Recommended)

# Create virtual environment
python -m venv venv
# Activate it# Windows:
venv\Scripts\activate
# macOS / Linux:source venv/bin/activate

Step 4 — Install Dependencies

pip install -r requirements.txt

💡 Tip: For the 6 training notebooks only, you just need the Section 1 packages. The full requirements.txt also includes Azure SDKs for when you move to actual labs.

Step 5 — Set Up Environment Variables (For AI-102 Labs)

# Copy the example file
cp .env.example .env
# Edit .env with your actual Azure credentials# Replace placeholder values with your keys and endpoints

Step 6 — Launch Jupyter Notebook

# Option A: Classic Jupyter
jupyter notebook
# Option B: JupyterLab
jupyter lab
# Option C: VS Code# Just open the .ipynb file — VS Code handles the rest

Step 7 — Start Learning!

Open 01_Python_Foundations.ipynb and begin. 🎉

📋 How to Use This as a Trainer

Suggested 6-Hour Schedule

Time	Notebook	Trainer Notes
Hour 1 (0:00–1:00)	01 — Python Foundations	Start slow. Let students type along. Explain f-strings thoroughly — they'll see them everywhere.
Hour 2 (1:00–2:00)	02 — Data Structures	Focus heavily on dictionaries and nested structures. This is the #1 skill for parsing API responses.
Hour 3 (2:00–3:00)	03 — Functions & Modules	Emphasize `os`, `json`, and `try/except`. Walk through the AI-102 error handling pattern.
Break (3:00–3:15)	☕ 15-minute break
Hour 4 (3:15–4:15)	04 — Files, JSON & .env	Do the `.env` section live — show how `load_dotenv()` works. This demystifies the first 5 lines of every lab.
Hour 5 (4:15–5:15)	05 — REST APIs	Draw the request/response diagram on a whiteboard. Explain headers, status codes.
Hour 6 (5:15–6:15)	06 — Complete AI-102 Pattern	This is the payoff. Walk through each pattern and show the equivalent code from an actual AI-102 lab repo.

Trainer Tips

Run every code cell live — don't just show slides
Encourage students to modify code — change variables, break things, see what happens
Connect to AI-102 constantly — after each concept, say "In the AI-102 lab, you'll see this when..."
Use the practice exercises — give students 3–5 minutes to try on their own
Notebook 6 is the bridge — after this, open an actual AI-102 lab and show students they can now read the code

🔗 AI-102 Lab Repositories (For After This Course)

Once students complete these 6 notebooks, they're ready for the official labs:

Repository	Covers
mslearn-ai-services	Azure AI Services (provisioning, security, monitoring)
mslearn-ai-vision	Computer Vision, Image Analysis, OCR
mslearn-ai-language	Text Analytics, NLP, Language Understanding
mslearn-ai-document-intelligence	Form Recognizer, Document Intelligence
mslearn-knowledge-mining	Azure AI Search, Knowledge Mining
mslearn-openai	Azure OpenAI, Generative AI

📖 Official Course Page:AI-102T00: Designing and Implementing a Microsoft Azure AI Solution

📖 Python Concepts ↔ AI-102 Labs Quick Map

For quick reference — which Python concept maps to which AI-102 lab activity:

Python Concept Where You'll See It in AI-102
───────────────────── ─────────────────────────────────
f-strings Building API URLs, printing results
dictionaries EVERY API response is a dictionary
nested dict/list Parsing sentiment scores, entities
json.loads() Parsing REST API response body
os.getenv() Loading endpoint & key from .env
try/except Wrapping every API call
requests.post() Calling REST APIs directly
with open("img","rb") Sending images to Vision API
for item in list Processing each detected entity
client.method() Using Azure SDK clients
pip install Installing Azure SDK packages

❓ Troubleshooting

Problem	Solution
`python` command not found	Use `python3` instead, or reinstall Python and check "Add to PATH"
`pip` command not found	Use `python -m pip install` instead
Jupyter won't start	Run `pip install jupyter notebook` then try again
`ModuleNotFoundError`	Run `pip install -r requirements.txt`
Notebooks won't open in VS Code	Install the "Jupyter" extension from Microsoft
`.env` values not loading	Make sure file is named `.env` (not `.env.example`) and is in the same folder
Azure API returns 401	Double-check your API key in the `.env` file
Azure API returns 403	Check your resource's region and endpoint URL

🏷️ GitHub Topics

Add these topics to your repo for discoverability:

python ai-102 azure microsoft-certification jupyter-notebook
azure-ai mct python-tutorial azure-ai-services cloud-computing

⭐ Support This Project

If this helped you or your students:

⭐ Star the repo — it helps others find it
🍴 Fork it — customize for your classroom
📢 Share it — tag someone prepping for AI-102

📜 License

This material is created for educational purposes. Feel free to use, modify, and distribute for training and learning.

🙏 Acknowledgments

Microsoft Learning AI-102 Lab Repositories — for the lab code patterns these notebooks are based on
Microsoft Learn — AI-102 Course — official course curriculum
AI-102 Study Guide — exam skills measured

Built with ❤️ to help every student succeed in AI-102.

Name		Name	Last commit message	Last commit date
labs		labs
.env.example		.env.example
.gitignore		.gitignore
LICENSE		LICENSE
Python_Programming_Essentials.pdf		Python_Programming_Essentials.pdf
Python_Programming_Essentials.pptx		Python_Programming_Essentials.pptx
README.md		README.md
requirements.txt		requirements.txt

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