AI ✨ & ML 🧠 Magic Playground

Learn the difference between REST and GraphQL, then peek at how a tiny ML model learns using colorful, friendly visuals. Built for curious school students — cute, clear, and hands-on.

Why This Matters 🌟

🚀

Modern API Skills

Learn REST & GraphQL through interactive examples and real-world scenarios that make sense.

🧠

AI/ML Fundamentals

Understand machine learning concepts with colorful visuals and simple explanations.

🎯

Hands-On Learning

Toggle between technologies, watch live charts, and interact with 3D visualizations.

REST API vs GraphQL

Flip the switch to compare fixed endpoints (REST) with flexible queries (GraphQL). Watch the example change! 🌈

RESTGraphQL

When to use REST

Simple resources (students, courses, scores)
Clear caching with URLs
Public APIs & microservices

When to use GraphQL

Fetch many related objects in one go
Ask for exactly the fields you need
Fewer round trips on slow networks

GET /api/students/42

Response:
{
  "id": 42,
  "name": "Asha",
  "grade": "10",
  "courses": [1,2,3]
}

Conceptual Try-It

Need student + courses + teacher names? With REST, you may call /students/:id, then /courses, then /teachers. With GraphQL, ask once for the exact fields.

REST calls: 3️⃣

GraphQL calls: 1️⃣

Detailed Comparison 📊

Feature	REST	GraphQL
Data Fetching	Multiple endpoints	Single endpoint
Over-fetching	Common issue	No over-fetching
Under-fetching	Common issue	No under-fetching
Learning Curve	Easy to start	Steeper learning
Caching	Built-in HTTP caching	Requires custom solution

Try It Yourself! 🎮

API Request Simulator

Select what data you need and see how many API calls each approach requires:

Basic Info

Course Details

Teacher Info

Grades & Scores

REST Approach

3️⃣

API Calls Required

GraphQL Approach

1️⃣

API Calls Required

Step-by-Step: Your AI/ML Quest

What is AI/ML?

AI makes computers act smart; ML teaches them from data. We'll learn by building a tiny model!

Collect Cute Data

Gather images or numbers (like emoji counts 🎈). Clean them and split into train/test.

Design a Model

Pick a recipe: linear model, decision tree, or a baby neural network. Choose inputs & targets.

Train!

Show examples → model guesses → compare with truth → nudge weights to improve (epochs).

Evaluate

Plot accuracy ↑ and loss ↓. If overfitting, add more data or regularize. Celebrate small wins!

Deploy & Share

Wrap your model in an API. Choose REST or GraphQL to deliver predictions to your app.