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 and Digital Technologies: Pioneering Healthcare Transformation takes a wide-angle view of the digital shift in health. You explore the technologies driving it together — AI, telehealth, wearables, health data platforms and automation — and how they combine to transform care delivery, operations and the patient experience. The course keeps sight of what makes health different: safety, equity, privacy and the change management real transformation needs. You finish with a grounded view of how digital technology is remaking healthcare. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AI and digital technologies pioneering healthcare transformation — the broad wave of digital tools reshaping care delivery, operations and patient experience.
📋 Course Objectives
1. Survey the digital technologies transforming health.
2. Combine AI, telehealth, wearables and data.
3. Reshape care delivery and operations.
4. Improve patient experience and access.
5. Address safety, equity and change.
👥 Who Should Enroll?
• Healthcare leaders and professionals
• Health-tech and digital teams
• Clinical and operations staff
• Students of digital health
🚀 Key Learning Outcomes
• A wide view of healthcare transformation.
• A digital-health systems perspective.
• A change-aware foundation.
• 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 artificial intelligence and machine learning concepts, including supervised and unsupervised learning techniques • Analyze mathematical foundations of AI, including linear algebra, calculus, and probability theory, to build a strong foundation for advanced AI concepts • Design and implement simple AI models using popular libraries and frameworks, such as TensorFlow or PyTorch, to gain hands-on experience with AI development
Data Engineering, Preprocessing, and Feature Pipelines
Configure and manage large datasets for AI model training, including data cleaning, preprocessing, and feature engineering techniques • Implement data pipelines using popular tools and technologies, such as Apache Beam or AWS Glue, to streamline data processing and integration • Evaluate and optimize data quality and feature relevance using statistical and machine learning techniques, such as correlation analysis and feature selection
Model Architecture, Algorithm Design, and Methods
Design and implement deep learning models, including convolutional neural networks and recurrent neural networks, for image and sequence data analysis • Develop and optimize AI algorithms, including gradient descent and stochastic gradient descent, to improve model performance and convergence • Analyze and compare different AI model architectures, including transfer learning and ensemble methods, to select the best approach for a given problem
Training, Hyperparameter Optimization, and Evaluation
Train and evaluate AI models using popular frameworks and libraries, including scikit-learn and TensorFlow, to develop a comprehensive understanding of model development and testing • Implement hyperparameter optimization techniques, including grid search and random search, to improve model performance and generalization • Configure and use popular evaluation metrics, including accuracy, precision, and recall, to assess model performance and identify areas for improvement
Deployment, MLOps, and Production Workflows
Deploy AI models in production environments, including cloud-based and on-premises deployments, using popular tools and technologies, such as Docker and Kubernetes • Implement MLOps practices, including model monitoring and maintenance, to ensure model performance and reliability in production environments • Develop and optimize production workflows, including data ingestion and processing, to streamline AI model deployment and integration
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze and mitigate bias in AI models, including data bias and algorithmic bias, using popular techniques and tools, such as fairness metrics and bias detection algorithms • Develop and implement responsible AI practices, including transparency and explainability, to ensure AI model trustworthiness and accountability • Evaluate and optimize AI model fairness and ethics, including data privacy and security, to ensure compliance with regulatory requirements and industry standards
Industry Integration, Business Applications, and Case Studies
Develop and implement AI solutions for real-world business problems, including customer segmentation and predictive maintenance, using popular AI technologies and tools • Analyze and evaluate AI case studies, including success stories and failure cases, to develop a comprehensive understanding of AI adoption and implementation in industry • Configure and use popular AI tools and platforms, including AI-powered CRM and ERP systems, to streamline business processes and improve operational efficiency
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | scikit-learn |
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