Programme Parameters
Foundation
3 Days (4.5 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
With the rapid growth of biological data from genomics, proteomics, and clinical studies, traditional analysis methods are often insufficient to uncover complex patterns. Machine learning provides powerful tools for classification, prediction, clustering, and biomarker discovery. R, being a leading language for statistical computing, offers a rich ecosystem of packages such as caret, randomForest, e1071, and Bioconductor for implementing ML workflows in biosciences. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
With the rapid growth of biological data from genomics, proteomics, and clinical studies, traditional analysis methods are often insufficient to uncover complex patterns. Machine learning provides powerful tools for classification, prediction, clustering, and biomarker discovery. R, being a leading language for statistical computing, offers a rich ecosystem of packages such as caret, randomForest, e1071, and Bioconductor for implementing ML workflows in biosciences.
📋 Course Objectives
1. Put biotechnology techniques to work on real datasets and case studies.
2. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 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
🚀 Key Learning Outcomes
• A portfolio-grade biotechnology deliverable you can defend and extend.
• 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
Encoding Biological and Chemical Entities
• SMILES, InChI, fingerprints and molecular graph representations
• Sequence encodings for nucleotides and proteins
• Descriptor choice and its dominance over model choice in small-data regimes
Property and Activity Modelling
• QSAR modelling and applicability domain estimation
• ADMET prediction and the endpoints where models remain unreliable
• Scaffold splitting instead of random splitting for honest evaluation
Learning From Omics Data
• High-dimension low-sample-size problems and regularisation
• Batch effects and correction methods that can destroy real signal
• Multi-omic integration and interpretability of the resulting features
Practical Research Workflows
• RDKit, scikit-learn and Bioconductor in a reproducible pipeline
• Experiment tracking and environment capture for a wet-lab collaboration
• Version control and data management for a research group
From Model to Publishable Claim
• External validation and the reproducibility crisis in biomedical ML
• Prospective testing against retrospective performance
• Reporting standards and reviewer expectations for ML in biomedical journals
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 |
Frequently Asked Questions
Scholar Feedback & Reviews
Based on 0 scholar submissions
Rating Breakdown
No verified reviews published yet. Be the first to share your academic experience.
Leave Scholar Feedback
Your rating will help prospective scholars. Ratings below 3 stars are routed privately to the faculty mentor for immediate response.
