Programme Parameters
Foundation
3 Weeks (30 Hrs)
Online (e-LMS)
• No prior experience required — basic computer literacy is sufficient.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Web Development Essentials with AI Integration teaches web building with an AI-first twist. You learn the essentials of a modern web app — front end, back end and APIs — and, crucially, how to integrate AI into it: calling AI and LLM services, embedding models, and designing intelligent features like search, chat, recommendations and automation. The course keeps the focus on wiring AI into a working product cleanly and responsibly. You finish able to build a web application with real AI features. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers web development essentials with AI integration — building modern web applications and wiring in AI services and models for intelligent features.
📋 Course Objectives
1. Build the essentials of a modern web app.
2. Design and consume APIs.
3. Integrate AI and LLM services.
4. Add intelligent features like chat and search.
5. Handle AI cost, latency and safety in apps.
👥 Who Should Enroll?
• Web developers adding AI
• Full-stack and product engineers
• Builders of AI-powered products
• Students of web and AI
🚀 Key Learning Outcomes
• The ability to build AI-integrated web apps.
• A product-oriented AI perspective.
• A working AI web project.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
AI Fundamentals, Mathematics, and Web Development Essentials Foundations
Apply mathematical concepts such as linear algebra and calculus to solve problems in AI and web development • Design and implement basic AI models using Python and relevant libraries • Evaluate the performance of AI models using metrics such as accuracy and precision
Data Engineering, Preprocessing, and Feature Pipelines
Develop data pipelines to preprocess and transform raw data into usable formats • Configure data storage solutions such as databases and data warehouses • Analyze data quality issues and implement data validation techniques
Model Architecture, Algorithm Design, and Web Development Essentials Methods
Implement deep learning models such as convolutional neural networks and recurrent neural networks • Design and optimize algorithmic solutions for web development problems • Integrate AI models with web applications using APIs and microservices
Training, Hyperparameter Optimization, and Evaluation
Train AI models using various optimization algorithms and hyperparameter tuning techniques • Evaluate the performance of AI models using cross-validation and other evaluation metrics • Configure and deploy AI models in cloud-based environments
Deployment, MLOps, and Production Workflows
Deploy AI models in production environments using containerization and orchestration tools • Develop and implement MLOps workflows to manage AI model lifecycles • Monitor and maintain AI models in production using logging and monitoring tools
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze and mitigate bias in AI models using fairness metrics and techniques • Develop and implement responsible AI practices such as transparency and explainability • Evaluate the ethical implications of AI systems and develop strategies for ethical AI development
Industry Integration, Business Applications, and Case Studies
Apply AI and web development concepts to real-world industry problems and case studies • Develop and implement AI-powered solutions for business applications • Evaluate the impact of AI and web development on business outcomes and revenue growth
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | JavaScript |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | React |
| Covered Tool / Platform | Node.js |
Frequently Asked Questions
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