AI with JavaScript & Node.js full syllabus

Module 1: Foundations of AI & JavaScript

Goal: Understand what AI is, and how JavaScript fits into AI development.

Topics:

• What is Artificial Intelligence?
• Types of AI: Narrow, General, Super AI
• Real-world use cases of AI
• Why JavaScript for AI?
• Introduction to Node.js & npm
• Setting up Node.js project (VS Code, Git, npm init)

Project: Simple command-line chatbot using basic JS logic

Module 2: Core JavaScript for AI

Goal: Master modern JavaScript features used in AI projects.

Topics:

• ES6+ Features (let/const, arrow functions, template literals)
• Arrays & Objects (looping, mapping, filtering)
• Promises, Async/Await
• Fetch API & HTTP Requests
• Working with JSON
• Error handling

Project: Data fetcher using API (e.g., weather or news)

Module 3: Math & Logic for AI in JS

Goal: Learn the basic math and logic needed for AI algorithms.

Topics:

• Arrays and matrix operations in JS
• Probability and statistics basics
• Normalization & scaling
• Random number generation
• Creating simple algorithms in JS

Mini-Project: Rock-Paper-Scissors bot that adapts to player behavior

Module 4: Machine Learning in JavaScript

Goal: Learn actual ML in JS using libraries.

Libraries Covered:

• TensorFlow.js – Deep learning
• Brain.js – Neural networks
• Synaptic – Simple neural networks
• ml5.js – Beginner-friendly AI in JS

Topics:

• Introduction to Machine Learning
• Training vs Testing
• Supervised vs Unsupervised learning
• Loading datasets in JS
• Training basic models

Project: Predict house prices using linear regression (TensorFlow.js)

Module 5: Working with TensorFlow.js

Goal: Build deep learning models in the browser or with Node.js

Topics:

• What is TensorFlow.js?
• Setting up browser vs Node.js environments
• Tensors and operations
• Creating a neural network
• Training and evaluating models
• Saving and loading models

Project: Handwriting digit recognizer using pre-trained MNIST model

Module 6: Natural Language Processing (NLP) with Node.js

Goal: Handle text, build intelligent bots.

Libraries:

• compromise
• natural
• nlp.js

Topics:

• Tokenization & stemming
• Sentiment analysis
• Named entity recognition
• Creating conversational agents
• Voice-based input (Web Speech API or Google Cloud)

Project: Voice-activated personal assistant

Module 7: AI APIs & Cloud AI Integration

Goal: Use cloud AI services to enhance your applications.

Platforms:

• OpenAI (ChatGPT, Whisper)
• Google Cloud AI
• HuggingFace APIs
• Microsoft Azure AI

Topics:

• REST API basics
• Working with API keys securely
• Sending/receiving AI data
• Handling JSON responses

Project: ChatGPT-powered Q&A Bot with Node.js + Express

Module 8: Real-Time AI Applications

Goal: Build interactive, real-time AI projects.

Topics:

• Real-time webcam input with JS
• Using WebSockets (socket.io)
• Integrating with TensorFlow.js models
• Live emotion detection
• Browser-based AI dashboards

Project: Real-time emotion detection app using webcam

Module 9: Hosting, Deployment & Optimization

Goal: Make your AI apps production-ready.

Topics:

• Hosting with Vercel, Netlify, or Heroku
• Express.js server deployment
• Environment variables
• Model optimization (quantization)
• Caching AI results
• Handling errors and fallback models

Project: Deploy your own AI chatbot on a public domain

Module 10: Final Capstone Projects

Goal: Build complete AI products.

Ideas:

• AI-Powered Resume Screener
• ChatGPT clone with custom training
• Visual Recognition System in browser
• Music genre predictor using ML