<!DOCTYPE html>
<html>
<head>
<title>Webpage Title</title>
</head>
<body>
<h1>Main Heading</h1>
<p>A paragraph text.</p>
</body>
</html>Learn to style web pages.
body {
background-color: #FFF;
font-size: 16px;
}
h1 {
font-size: 2rem;
}
p {
font-family: Verdana,sans-serif;
}Learn to build interactive web pages.
<button onclick="demoFunc()">Click Me!</button>
<script>
function demoFunc() {
let p = document.getElementById("p");
p.textContent = "You changed this text!";
p.style.fontSize = "20px";
p.style.color = "blue";
}
</script>Learn to build fast and scalable server-side applications with Node.js.
const express = require('express');
const app = express();
app.get('/', (req, res) => {
res.send('Hello Express.js!');
});
app.listen(3000, () => {
console.log('Server running on port 3000');
});
Learn to build dynamic web content.
<?php
echo "Hello, I'm a PHP script!";
function demoFunc()
{
echo "It works.";
echo "Today's date is " . date('Y-m-d');
echo "Have a great day!";
}
demoFunc();
?>Learn to manage and manipulate relational and NoSQL databases efficiently.
-- Retrieve active users
SELECT userID,
CONCAT(firstName, ' ', lastName) AS name,
email,
registrationDate
FROM users
WHERE status = 1;
-- End of query
-- Output: userID, name, email, registrationDateLearn to develop diverse software with simplicity and readability.
def greet():
return '''Welcome to Python!
It's easy to learn.'''
message = greet()
print(message)Learn to create cross-platform applications and enterprise software.
public class Greet {
static String greet() {
return "Welcome to Java!";
}
public static void main(String[] args) {
System.out.println(greet());
System.out.println("It's easy to learn.");
}
}<%@ page contentType="text/html; charset=UTF-8"%>
<html>
<head>
<title>Quick JSP Demo</title>
</head>
<body>
<p>Hello, I'm a JSP script!</p>
<% out.println("Today's date is " + new java.util.Date()); %>
</body>
</html>Learn to create systems software and understand low-level computing.
#include<stdio.h>
int main()
{
printf("First C Program.\n");
return 0;
}Learn to create systems software with object-oriented capabilities.
#include <iostream>
int main()
{
std::cout<<"First C++ Program.";
std::cout<<std::endl<<"Its easy to learn.";
}Learn to structure and exchange data efficiently with JSON and XML.
{
"user": {
"name": "User name",
"active": true
}
<user>
<name>User name</name>
<active>true</active>
</user>
Understand how an AI model requests an approved tool, how the application validates and runs it, and how to handle results safely.
Learn how to request JSON from an AI model, validate required fields and allowed values, and check meaning as well as syntax.
Understand how RAG retrieves relevant passages, adds them to a question, and generates grounded answers, with a simple Python retrieval example.
Learn to write AI prompts with a clear task, context, limits, and output format, then improve them by testing real responses.
Understand embeddings, vector similarity, and cosine scores with a runnable Python example, practical use cases, and retrieval pitfalls.
Learn how tokens and context windows work, why long conversations can drift, and how to choose useful context for AI prompts.
Learn how a confusion matrix, accuracy, precision, recall, and F1 explain classification errors with a worked spam-filter example and runnable Python code.
Understand underfitting, overfitting, training and validation errors, and practical ways to improve generalization with a worked comparison and diagram.
Follow a small neural network from inputs through weights, biases, and activation functions to an output score, with a diagram and runnable Python example.
Learn why AI models need training, validation, and test data. Follow a runnable Python example and avoid data leakage and misleading scores.
Learn supervised, unsupervised, and reinforcement learning with simple examples. Discover how feedback, labels, and rewards shape each method.
Learn how AI datasets, features, and labels work. Explore a simple example, spot data leakage, and prepare better data for machine learning.
Understand how AI, machine learning, deep learning, and generative AI relate. Compare their purposes, examples, limits, and when each term fits.
Learn artificial intelligence from the basics. Understand AI systems, common uses, key terms, limits, and a clear path to machine learning and generative AI.
Learn how AI systems turn inputs into outputs. Follow a clear workflow for data, models, inference, evaluation, and human review with practical examples.
Learn machine learning basics with a worked example. Understand features, labels, training, testing, inference, overfitting, and simple baselines.
Learn generative AI basics: how prompts, tokens, models, and context produce text or images, plus practical steps to check accuracy and protect data.
Learn responsible AI basics. Check accuracy, bias, privacy, and security risks, set human review rules, and monitor AI systems after release.
Learn Python itertools.pairwise() to compare adjacent values, track changes, and handle short iterables with clear, runnable examples.