Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI-Powered Customer Experience shows how machine learning turns customer data into better, more personal interactions. You build the models behind modern CX: recommendation and personalisation engines, sentiment and feedback analysis, conversational AI and chatbots, and churn prediction to retain customers before they leave. The course maps these to the customer journey and keeps sight of what matters — measurable experience and business impact — as well as the trust and privacy considerations that responsible personalisation demands. You finish able to apply AI to a real customer-experience problem. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course applies AI to customer experience — personalisation, recommendation, sentiment analysis, chatbots and churn prediction across the customer journey.
📋 Course Objectives
1. Build recommendation and personalisation models.
2. Analyse customer sentiment and feedback.
3. Design conversational AI and chatbots.
4. Predict and reduce customer churn.
5. Balance personalisation with trust and privacy.
👥 Who Should Enroll?
• CX, marketing and product professionals
• Data scientists in customer analytics
• Business and operations analysts
• Students specialising in applied business AI
🚀 Key Learning Outcomes
• The ability to apply AI across the customer journey.
• A CX analytics or personalisation project.
• A business-impact-focused 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
AI Fundamentals and Mathematics
Apply linear algebra and calculus concepts to solve AI-related problems • Analyze probability distributions and statistical models for data analysis • Develop mathematical models to represent complex customer experience systems
Data Engineering and Preprocessing
Design data pipelines to handle large-scale customer experience data • Configure data preprocessing techniques to handle missing values and outliers • Implement data quality control measures to ensure accurate analysis
Model Architecture and Algorithm Design
Evaluate different AI model architectures for customer experience applications • Develop custom AI algorithms to solve specific customer experience problems • Optimize model performance using hyperparameter tuning techniques
Training, Hyperparameter Optimization, and Evaluation
Train AI models using large-scale customer experience datasets • Implement hyperparameter optimization techniques to improve model performance • Evaluate model performance using metrics such as accuracy and F1-score
Deployment, MLOps, and Production Workflows
Deploy AI models in production environments using cloud-based services • Configure MLOps pipelines to automate model deployment and monitoring • Develop production-ready workflows to integrate AI models with existing systems
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze AI models for bias and fairness using statistical techniques • Develop strategies to mitigate bias and ensure responsible AI practices • Implement transparency and explainability techniques to improve AI model trustworthiness
Industry Integration, Business Applications, and Case Studies
Apply AI-powered customer experience solutions to real-world business problems • Evaluate the impact of AI on customer experience metrics such as satisfaction and loyalty • Develop business cases to justify the adoption of AI-powered customer experience solutions
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
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