Programme Parameters
Foundation
4 Weeks (40 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
AI in Retail and E-commerce shows how machine learning drives modern shopping, online and in-store. You learn to build the models behind it: recommendation engines that lift sales, demand forecasting that keeps shelves and warehouses right, dynamic pricing, and personalisation across the customer journey. The course also covers customer analytics — segmentation, churn and lifetime value — and the operational data that connects it all. Grounded in real retail problems, it shows where AI genuinely moves the needle. You finish able to apply AI to a retail or e-commerce challenge. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AI in retail and e-commerce — recommendation, demand forecasting, dynamic pricing, personalisation and customer analytics.
📋 Course Objectives
1. Build recommendation and personalisation engines.
2. Forecast demand across products and locations.
3. Apply dynamic pricing strategies.
4. Analyse customers: segmentation, churn and value.
5. Connect models to retail operations.
👥 Who Should Enroll?
• Retail and e-commerce professionals
• Data scientists in commerce
• Merchandising and pricing analysts
• Students of retail analytics
🚀 Key Learning Outcomes
• The ability to apply AI in retail.
• A recommendation or forecasting project.
• A commercially focused analytics approach.
• 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
Module 1 — Foundations of AI in Retail and E-commerce
Introduction to AI in retail and digital commerce • Evolution from traditional models to intelligent commerce ecosystems • Role of AI in customer experience and operations • Drivers of AI adoption in modern retail
Module 2 — Retail Data, Consumer Behavior, and Digital Intelligence
Sources of retail and e-commerce data • Customer journey mapping and behavioral analytics • Data collection from transactions and engagement signals • Challenges in data quality and personalization readiness
Module 3 — Machine Learning for Retail Analytics
Customer segmentation and demand forecasting • Sales prediction and predictive analytics • Recommendation systems and marketing strategies • Performance evaluation of retail AI models
Module 4 — AI for Personalization and Customer Experience
Conversational AI and virtual shopping assistants • Dynamic pricing and promotion optimization • Customer sentiment analysis and loyalty enhancement • Product recommendation tailored shopping experiences
Module 5 — Inventory, Supply Chain, and Smart Retail Operations
AI-driven inventory optimization and replenishment • Demand sensing and supply chain forecasting • Warehouse automation and logistics intelligence • Operational efficiency: reducing stockouts and overstock
Module 6 — Computer Vision and Intelligent Store Technologies
Computer vision for shopper behavior monitoring • Automated checkout and shelf monitoring • Visual search and image-based product discovery • Omnichannel retail integration strategies
Module 7 — Fraud Detection, Trust, and Responsible AI
Fraud detection in transactions and payments • AI for cybersecurity and secure interactions • Ethical concerns in consumer profiling • Privacy, transparency, and responsible AI practices
Module 8 — Applications, Case Studies, and Future Trends
Generative AI in product content and digital merchandising • Autonomous retail and immersive commerce trends • Case studies in e-commerce personalization • Future of intelligent marketplaces and V-commerce
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python (pandas, scikit-learn, PyTorch) |
| Covered Tool / Platform | Recommendation Engines (Collaborative Filtering, DeepRec) |
| Covered Tool / Platform | Computer Vision (YOLO, TensorFlow Lite) |
| Covered Tool / Platform | Demand Forecasting Models (ARIMA, Prophet, LSTM) |
| Covered Tool / Platform | Generative AI APIs for Content Creation |
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