🤖 AI for Teens Bootcamp Course Guide

A hands-on, beginner-friendly course for teen students. Work through each section in order every topic builds on the one before it.

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🗺️ Course Outline

#	Section	What's Inside
1	🐍 Intro to Python	4 notebooks
2	📊 Data Literacy	2 notebooks + 1 workbook
3	🤖 Machine Learning	1 notebook + 1 workbook
4	💬 Natural Language Processing (NLP)	1 notebook + 1 workbook
5	👁️ Computer Vision	1 notebook + 1 workbook
6	✨ Basic Intro to Generative AI	1 notebook + 1 workbook + Python scripts

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⚡ Quick Navigation

Click Open notebook to view on GitHub, or click the Colab badge to run it instantly in your browser — no installation needed!

🐍 Section 1: Intro to Python

#	Topic	GitHub	Google Colab
1	Fundamental Programming Concepts	Open notebook
2	Program Logic and Control Flow	Open notebook
3	Data Structures and Functions	Open notebook
4	File Paths and File System	Open notebook

📊 Section 2: Data Literacy

#	Topic	GitHub	Google Colab
5	Working with Data: Pandas	Open notebook
5b	Data Analysis Workbook	Open workbook
5c	Creating Dashboards with Streamlit	Open notebook

🤖 Section 3: Machine Learning

#	Topic	GitHub	Google Colab
6	Machine Learning Notebook	Open notebook
6b	Machine Learning Workbook	Open workbook

💬 Section 4: Natural Language Processing (NLP)

#	Topic	GitHub	Google Colab
7	NLP Notebook	Open notebook
7b	NLP Workbook	Open workbook

👁️ Section 5: Computer Vision

#	Topic	GitHub	Google Colab
8	Computer Vision Notebook	Open notebook
8b	Computer Vision Workbook	Open workbook

✨ Section 6 — Basic Intro to Generative AI

#	Topic	GitHub	Google Colab
9	Intro to GenAI Notebook	Open notebook
9b	GenAI Workbook	Open workbook

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🚀 How to Run the Notebooks

☁️ Option 1: Google Colab (Easiest, No Setup Needed!)

This is the recommended way for most students.

1. Click any Colab badge from the Quick Navigation tables above.
2. Sign in with your Google account.
3. Click "Copy to Drive" so your changes are saved.
4. Run cells with Shift + Enter and you're good to go! 🎉

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💻 Option 2: Run on Your Computer

What you need:

• Python 3.8+
• VS Code or Jupyter Notebook

Steps:

# 1. Clone the repository
git clone https://github.com/Publica-AI/cchub-AI_for_teens_bootcamp.git

# 2. Move into the folder
cd cchub-AI_for_teens_bootcamp

# 3. Create a virtual environment (recommended)
python -m venv venv
source venv/bin/activate        # Mac / Linux
venv\Scripts\activate           # Windows

# 4. Install all required packages
pip install -r requirements.txt

# 5. Launch Jupyter
jupyter notebook

A browser window will open. Click any folder, then click a .ipynb file to open it.

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📦 What is requirements.txt?

The requirements.txt file lists every Python package this course needs — things like pandas, matplotlib, scikit-learn, tensorflow, and more. Installing it once sets up everything for all sections.

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⚠️ If You Get Version or Dependency Errors

Sometimes packages clash with each other or with your Python version. Here are three ways to fix it:

Option A — Install without strict versions (most flexible)

Instead of pinning exact versions, install the packages by name only:

pip install pandas matplotlib seaborn scikit-learn tensorflow keras pillow requests openai jupyter notebook streamlit

This lets pip choose versions that work together on your machine.

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Option B — Use a fresh virtual environment

A clean environment avoids conflicts with anything already installed on your computer:

# Create a brand new environment
python -m venv fresh_env

# Activate it
source fresh_env/bin/activate     # Mac / Linux
fresh_env\Scripts\activate        # Windows

# Install from requirements.txt
pip install -r requirements.txt

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Option C — Use conda (if you have Anaconda or Miniconda)

conda create -n teens_ai python=3.10
conda activate teens_ai
pip install -r requirements.txt

💡 Quick tip: If one specific package fails, skip it and install the rest. Most notebooks will still work. Only install what that section actually needs.

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🖥️ Option 3: VS Code

1. Install the Jupyter extension in VS Code (ms-toolsai.jupyter).
2. Open the cloned folder in VS Code.
3. Click any .ipynb file — it opens directly in VS Code.
4. Pick a Python kernel from the top-right dropdown and run cells with Shift + Enter.

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📖 Notebook vs Workbook: What's the Difference?

📘 Notebook	📝 Workbook
Teaches the concept step by step	Applies the concept to a real project
Includes explanations and examples	Includes student challenges and tasks
Complete thisfirst	Complete this after the notebook

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📂 Section Breakdown

🐍 Section 1: Intro to Python

Folder: Intro_to_Python/

Start here if you are new to coding. These four notebooks cover the basics of Python.

#	File	What you will learn
1	fundamental_programming_concepts.ipynb	print(), input(), variables, data types, comments
2	program_logic_and_control_flow.ipynb	Operators,if/elif/else, for loops, while loops
3	data_structures_and_functions.ipynb	Lists, tuples, dictionaries, functions, return values
4	file_paths_and_file_system.ipynb	File paths, reading and writing files, directories

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📊 Section 2: Data Literacy

Folder: Data_literacy/

Learn how to explore and visualise real data using pandas and Streamlit.

File	What it covers
working_with_data_pandas.ipynb	Loading, cleaning, filtering, grouping, and charting data
data_analysis_workbook.ipynb	Work with a real dataset of 2,392 student records
creating_dashboards_with_streamlit.ipynb	Build interactive dashboards with Streamlit

Data folder: Data_literacy/data/

File	Description
kenyan_student_data.csv	Kenyan student dataset used in the pandas notebook
Student_performance_data.csv	Real dataset of 2,392 student records used in the workbook
student_data_dictionary.md	Explains what each column in the dataset means

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🤖 Section 3: Machine Learning

Folder: Machine Learning/

Learn how machines learn from data and how to build simple prediction models.

File	What it covers
machine_learning_notebook.ipynb	What ML is, types of ML, training and testing models, key algorithms
ml_workbook.ipynb	Build a real ML project from data to prediction, with student tasks

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💬 Section 4: Natural Language Processing (NLP)

Folder: NLP/

Learn how computers understand and work with human language — like detecting emotions in text!

File	What it covers
nlp_notebook.ipynb	Tokenisation, stopwords, sentiment analysis, text classification
nlp_workbook.ipynb	Apply NLP to a real text dataset, with student challenges

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👁️ Section 5 — Computer Vision

Folder: Computer_Vision/

Learn how computers see and understand images — like identifying cats, dogs, and people in photos!

File	What it covers
cv_notebook.ipynb	How images work, loading and processing images, object detection basics
cv_workbook.ipynb	Apply computer vision to real images, with student challenges

Image folder: Computer_Vision/image/

File	Description
boy.jpg, boy2.jpg	Sample photos of people
girl.jpg, girl2.jpg	Sample photos of people
cat.jpg	Sample photo of a cat
dog.jpg, dog2.jpg	Sample photos of a dog
backpack.jpg	Sample photo of an object
dress.jpg, shirt.jpg, shirt2.jpg	Sample photos of clothing
knife.jpg	Sample photo of an object

These images are used in the notebook and workbook exercises — no need to download anything extra.

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✨ Section 6: Basic Intro to Generative AI

Folder: Basic_Intro_to_GenAI/

Learn what LLMs and chatbots are, and build your own!

File	What it covers
notebook.ipynb	What chatbots and LLMs are, how they work, OpenAI and Ollama
workbook.ipynb	Build and customise chatbots, student challenges

Python scripts:

Script	What it does
ask_openai.py	Send a question to OpenAI from the terminal
ask_ollama.py	Send a question to Ollama (works offline) from the terminal
openai_chatbot_app.py	A full chatbot app powered by OpenAI
ollama_chatbot_app.py	A full chatbot app powered by Ollama (runs offline)

⚠️ The GenAI scripts need an API key (for OpenAI) or Ollama installed (for offline use). Full instructions are inside the notebooks.

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🗂️ Repository Structure

cchub-AI_for_teens_bootcamp/
│
├── README.md
├── LICENSE
├── requirements.txt
│
├── Intro_to_Python/
│   ├── fundamental_programming_concepts.ipynb
│   ├── program_logic_and_control_flow.ipynb
│   ├── data_structures_and_functions.ipynb
│   └── file_paths_and_file_system.ipynb
│
├── Data_literacy/
│   ├── working_with_data_pandas.ipynb
│   ├── data_analysis_workbook.ipynb
│   ├── creating_dashboards_with_streamlit.ipynb
│   └── data/
│       ├── kenyan_student_data.csv
│       ├── Student_performance_data.csv
│       └── student_data_dictionary.md
│
├── Machine Learning/
│   ├── machine_learning_notebook.ipynb
│   └── ml_workbook.ipynb
│
├── NLP/
│   ├── nlp_notebook.ipynb
│   └── nlp_workbook.ipynb
│
├── Computer_Vision/
│   ├── cv_notebook.ipynb
│   ├── cv_workbook.ipynb
│   └── image/
│       ├── boy.jpg, boy2.jpg
│       ├── girl.jpg, girl2.jpg
│       ├── cat.jpg
│       ├── dog.jpg, dog2.jpg
│       ├── backpack.jpg
│       ├── dress.jpg, shirt.jpg, shirt2.jpg
│       └── knife.jpg
│
└── Basic_Intro_to_GenAI/
    ├── notebook.ipynb
    ├── workbook.ipynb
    ├── ask_openai.py
    ├── ask_ollama.py
    ├── openai_chatbot_app.py
    └── ollama_chatbot_app.py

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⌨️ Keyboard Shortcuts

Action	Shortcut
Run the current cell	Shift + Enter
Run and add a new cell below	Alt + Enter
Add a cell below	B (command mode)
Delete a cell	D D (command mode)
Stop a running cell	Click the ■ Stop button

💡 Tip: Click a cell to select it (command mode). Press Enter to type inside it (edit mode). Press Escape to go back to command mode.

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📄 License

See LICENSE for details.