Programme Parameters
Graduate / Intermediate
3 Days (4.5 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
This 3-day course focuses on leveraging AI and Machine Learning techniques in healthcare and clinical sciences. Machine learning has revolutionized multiple domains, and its application in healthcare can significantly enhance clinical decision-making processes. Participants will explore foundational concepts, algorithms, and tools for building AI/ML models, focusing on supervised and unsupervised learning techniques. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This 3-day course focuses on leveraging AI and Machine Learning techniques in healthcare and clinical sciences. Machine learning has revolutionized multiple domains, and its application in healthcare can significantly enhance clinical decision-making processes. Participants will explore foundational concepts, algorithms, and tools for building AI/ML models, focusing on supervised and unsupervised learning techniques.
📋 Course Objectives
1. Develop hands-on skill in unsupervised learning techniques.
2. Translate biotechnology theory into practical, reproducible analysis.
3. Assemble a documented case study that evidences your applied capability.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
• Data and computational scientists moving into unsupervised learning techniques
🚀 Key Learning Outcomes
• Confidence to reason about unsupervised learning techniques in real projects.
• Tangible, reproducible biotechnology work to show supervisors or employers.
• 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
Clinical Data and Its Difficulties
• EHR structure, coding systems such as ICD and SNOMED, and their inconsistency
• Missingness that is informative, because a test not ordered carries meaning
• De-identification, HIPAA and Indian data protection obligations
Supervised and Unsupervised Methods
• Risk prediction, diagnosis support and phenotyping as distinct problems
• Outcome definition, look-ahead bias and the label leakage endemic to EHR work
• Clustering for patient subgroups and the fragility of the resulting clusters
Evidence a Clinician Would Accept
• Discrimination against calibration, and why calibration decides clinical usefulness
• External and temporal validation, and performance drop across sites
• Decision curve analysis and net benefit over existing practice
Bias and Failure
• Documented cases where a clinical model encoded a resource proxy as need
• Subgroup performance reporting rather than a single aggregate metric
• Automation bias and the effect of a wrong recommendation on a clinician
Into the Clinic
• Software as a Medical Device, CDSCO and FDA pathways in outline
• Workflow integration, alert fatigue and the reason good models go unused
• Post-deployment monitoring, drift and the governance that owns it
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Pandas |
| Covered Tool / Platform | NumPy |
| Covered Tool / Platform | Matplotlib |
| Covered Tool / Platform | XGBoost |
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