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 for Sustainable Urban Mining explores how machine learning helps reclaim the ore hidden in cities: the metals and materials locked in electronic and urban waste. You learn how AI improves each stage of urban mining — computer vision for sorting and identifying materials, characterising complex waste streams, and optimising recovery and recycling processes. The course sets this within the circular-economy imperative to recover critical materials rather than mine and discard them, and the real operational challenges of doing so. You finish able to reason about applying AI to a material-recovery problem. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course applies AI to urban mining — recovering valuable materials from e-waste and urban waste streams through intelligent sorting, characterisation and process optimisation.
📋 Course Objectives
1. Explain urban mining and its material value.
2. Apply computer vision to waste sorting and identification.
3. Characterise complex e-waste streams with AI.
4. Optimise material-recovery and recycling processes.
5. Connect recovery to circular-economy goals.
👥 Who Should Enroll?
• Recycling and waste-recovery professionals
• Sustainability and materials engineers
• Data scientists in the circular economy
• Students of resource recovery
🚀 Key Learning Outcomes
• An understanding of AI in urban mining.
• A material-recovery analytics project.
• A circular-economy-oriented 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, Mathematics, and Foundations
Develop a comprehensive understanding of AI concepts, including machine learning, deep learning, and neural networks • Analyze mathematical prerequisites for AI, including linear algebra, calculus, and probability theory • Design a basic AI model using Python and relevant libraries, such as NumPy and Pandas
Data Engineering, Preprocessing, and Feature Pipelines
Configure data pipelines using Apache Beam and Google Cloud Dataflow for efficient data processing • Implement data preprocessing techniques, including data cleaning, feature scaling, and feature engineering • Evaluate the effectiveness of different data preprocessing methods using metrics such as accuracy and F1-score
Model Architecture, Algorithm Design, and Methods
Design and implement convolutional neural networks (CNNs) for image classification tasks • Develop and train recurrent neural networks (RNNs) for sequence prediction tasks • Optimize model architecture using techniques such as transfer learning and hyperparameter tuning
Training, Hyperparameter Optimization, and Evaluation
Train AI models using popular frameworks such as TensorFlow and PyTorch • Implement hyperparameter optimization techniques, including grid search and random search • Evaluate model performance using metrics such as precision, recall, and area under the ROC curve
Deployment, MLOps, and Production Workflows
Deploy AI models using cloud platforms such as AWS SageMaker and Google Cloud AI Platform • Implement continuous integration and continuous deployment (CI/CD) pipelines using tools such as Jenkins and GitLab • Configure model monitoring and logging using tools such as Prometheus and Grafana
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze the ethical implications of AI systems, including bias, fairness, and transparency • Develop strategies for mitigating bias in AI systems, including data preprocessing and model regularization • Implement responsible AI practices, including model interpretability and explainability
Industry Integration, Business Applications, and Case Studies
Evaluate the business value of AI solutions, including cost-benefit analysis and return on investment (ROI) calculation • Develop AI-powered solutions for real-world business problems, including customer segmentation and predictive maintenance • Analyze case studies of successful AI implementations in various industries, including healthcare and finance
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Apache Beam |
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