Instructor-led 4-days

Course Description:

This four-day, immersive course introduces participants to the intersection of web development and artificial intelligence, empowering them to build intelligent, data-driven web applications. Through hands-on projects, interactive lectures, and practical exercises, students will explore core AI principles, web development frameworks, machine learning integration, and deployment strategies for creating AI-enhanced web applications. By the end of the course, participants will have a foundational understanding of building and deploying AI-powered web applications.

Learning Objectives:

By the end of this course, participants will be able to:

1. Understand and apply essential AI concepts relevant to web development.
2. Develop a dynamic web application using modern frameworks.
3. Integrate machine learning models into web applications for real-time predictions and recommendations.
4. Deploy and maintain AI-powered web applications with best practices in performance optimization, scalability, and security.
5. Address ethical and user-centric design considerations when deploying AI in web applications.

Part 1: Foundations of AI in Web Development

Module 1:

1. Introduction to Web Application Development
   • Overview of web application architecture
   • Brief on front-end and back-end technologies (HTML, CSS, JavaScript, Python, Node.js, etc.)
2. Introduction to Artificial Intelligence in Web Development
   • AI, Machine Learning (ML), and Deep Learning (DL) basics
   • Use cases of AI in web applications (e.g., personalization, predictive analytics)
3. Setting Up the Development Environment
   • Installing necessary software (Python, Flask/Django, Node.js, etc.)
   • Introduction to version control with Git and GitHub

Module 2:

4. Basic Machine Learning Models

• Overview of common machine learning models (classification, regression, clustering)
• Hands-on session: Building a basic ML model in Python

5. Integrating ML Models with Web Applications
   • Using REST APIs for ML model deployment
   • Hands-on session: Integrating a simple ML model with a Flask app

Outcome:
Participants will understand the fundamental principles of AI and web development, and they’ll develop a simple web application with a basic ML model.

Part 2: Building and Training AI Models for Web Applications

Module 1:

1. Data Collection and Preprocessing
   • Understanding data types, sources, and formats (CSV, JSON, databases)
   • Data cleaning and preparation for training ML models
   • Hands-on session: Preparing a sample dataset
2. Training and Evaluating Machine Learning Models
   • Splitting data into training, validation, and testing sets
   • Model training, evaluation, and improvement techniques (cross-validation, tuning)

Module 2:
3. Advanced Model Integration with Web Applications

• Setting up RESTful APIs for model prediction
• Implementing asynchronous calls for AI processing in real time

4. Front-End Development for AI Interactivity
   • Building interactive front-end components (React, Vue.js basics)
   • Connecting front-end with the back-end for AI response display

Outcome:
Participants will gain hands-on experience in training, evaluating, and deploying ML models, with an understanding of integrating model predictions into the front-end interface.

Part 3: Enhancing User Experience with AI-Powered Features

Module 1:

1. Personalization and Recommendation Systems
   • Types of recommendation systems (collaborative filtering, content-based, hybrid)
   • Hands-on session: Building a recommendation engine for a sample application
2. Natural Language Processing (NLP) for Web Applications
   • Overview of NLP and its applications in web (e.g., chatbots, sentiment analysis)
   • Hands-on session: Building a simple sentiment analysis tool using NLP

Module 2:
3. Real-Time Analytics and Predictive Modeling

• Understanding real-time data processing (e.g., user behavior tracking)
• Hands-on session: Implementing real-time data analytics on user interactions

4. User-Centric AI Design
   • Ethical considerations, bias in AI models, and user privacy
   • Discussing transparency and accountability in AI applications

Outcome:
Participants will learn how to incorporate personalized AI features and real-time analytics into web applications, enhancing the user experience with interactive and intelligent features.

Part 4: Deployment and Maintenance of AI-Powered Web Applications

Module 1:

1. Deploying AI Models with Web Applications
   • Introduction to cloud services (AWS, GCP, Azure) and model deployment
   • Hands-on session: Deploying a web application with AI model on a cloud platform
2. Scalability and Performance Optimization
   • Optimizing AI models for production (model compression, caching strategies)
   • Techniques for scaling web applications (load balancing, database optimization)

Module 2:
3. Securing AI Web Applications

• Implementing authentication and authorization
• Security best practices for handling data and user interactions

4. Final Project and Presentation
   • Hands-on session: Building and deploying a fully functional AI-powered web application
   • Presentation of projects and peer feedback

Outcome:
By the end of the day, participants will be able to deploy an AI-powered web application on a cloud platform, with knowledge of scaling, security, and maintenance practices.