Resources

A structured AI resource pathway for youth.

A curated reference path that moves from AI literacy and mathematics to programming, machine learning, generative AI, and real portfolio projects.

Suggested path: Understand AI → build math fluency → code with data → train models → use generative AI responsibly → build and present a project.
01

AI Literacy & Responsible Use

Build a clear mental model of what AI can and cannot do before using advanced tools.

  • AI, machine learning, deep learning, and generative AI
  • Everyday AI systems: search, recommendations, vision, language
  • Bias, privacy, hallucinations, copyright, and academic integrity
02

Mathematics for AI

Build the math foundation that makes models understandable instead of mysterious.

  • Functions, graphs, rates of change, and optimization
  • Linear algebra: vectors, matrices, dot products, embeddings
  • Probability and statistics: distributions, uncertainty, evaluation
03

Python, Data & Experimentation

Move from using AI tools to building small, testable AI experiments.

  • Python basics, notebooks, variables, loops, functions
  • Data cleaning, tables, charts, and simple analysis
  • Train/test split, metrics, and reproducible experiments
04

Machine Learning Foundations

Understand how computers learn patterns from examples and how to judge results.

  • Regression, classification, clustering, and model selection
  • Gradient descent, loss functions, overfitting, and validation
  • Practical tools such as scikit-learn and Google Colab
05

Generative AI & Creative Tools

Use language, image, and multimodal AI systems with stronger judgment.

  • Prompt design, role prompting, examples, and evaluation
  • AI for writing, design, presentations, coding, and research
  • Human review, source checking, and transparent AI use
06

Capstone Projects & Portfolio

Turn exploration into something students can explain, demo, and improve.

  • Build a chatbot, image classifier, data story, or school helper
  • Write a project brief: problem, data, model, limits, ethics
  • Present the project and reflect on what should be improved

Selected AI References

Trusted resources students can explore beyond workshops.

These are well-known public references from universities, major AI teams, and open-source knowledge projects.

AI Literacy

Elements of AI

A friendly introduction to what AI is, what it can do, and how it affects society. Good first course for students and parents.

Open course

Beginner Reference

Microsoft AI for Beginners

A 12-week, 24-lesson open reference covering core AI ideas with practical examples and classroom-friendly structure.

Open resource

Mathematics

Mathematics for ML & Data Science

DeepLearning.AI's math path for linear algebra, calculus, probability, and statistics used in machine learning.

Open course

Machine Learning

Andrew Ng's Machine Learning Specialization

The classic beginner-friendly machine learning path from DeepLearning.AI and Stanford Online, available on Coursera.

Open course

Hands-on ML

Google Machine Learning Crash Course

A practical self-study reference with videos, visual explanations, and exercises for core ML concepts.

Open course

Math Practice

Khan Academy Math Foundations

Use linear algebra, statistics, probability, algebra, and calculus lessons to strengthen the math needed for AI.

Open lessons
ChatGPT Google Colab Scratch Teachable Machine Python Canva AI Hugging Face

These tools are shared for reference purposes. Students should use AI tools responsibly and with guidance from parents or mentors when needed.