🐍 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.

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📌 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  │                        │
│                  └─────────────────────┘                        │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

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🎯 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

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📂 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

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🗺️ 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

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📓 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

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📓 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

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📓 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

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📓 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

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📓 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

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💻 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

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🛠️ 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

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🚀 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. 🎉

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📋 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

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🔗 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

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📖 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

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❓ 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

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🏷️ 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

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⭐ 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

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📜 License

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

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🙏 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

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