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
The integration of AI and machine learning with bioinformatics has transformed how researchers process, analyze, and interpret biological data. Genomic sequencing, proteomics data, and clinical records are growing at an exponential rate, making traditional methods inadequate for understanding complex patterns and relationships. AI tools such as deep learning, random forests, and support vector machines are now essential for making sense of vast datasets in a meaningful way. Across 4 Weeks, you will work hands-on with deep learning and random forests, then consolidate everything in a capstone project. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
The integration of AI and machine learning with bioinformatics has transformed how researchers process, analyze, and interpret biological data. Genomic sequencing, proteomics data, and clinical records are growing at an exponential rate, making traditional methods inadequate for understanding complex patterns and relationships. AI tools such as deep learning, random forests, and support vector machines are now essential for making sense of vast datasets in a meaningful way.
📋 Course Objectives
1. Build practical fluency in deep learning.
2. Gain working command of random forests.
3. Put bioinformatics techniques to work on real datasets and case studies.
4. Produce a reproducible, portfolio-ready project you can cite in a thesis, paper, or job application.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in bioinformatics
• R&D engineers and working professionals applying bioinformatics in industry
• Academics and educators building research or teaching capacity in bioinformatics
• Data and computational scientists moving into deep learning
🚀 Key Learning Outcomes
• Confidence to implement deep learning in real projects.
• Confidence to reason about random forests in real projects.
• A portfolio-grade bioinformatics 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
A Working Bioinformatics Setup
• Conda environments, containers and reproducible tool installation
• Command-line fluency for sequence and tabular data manipulation
• Workflow managers: Nextflow or Snakemake for pipelines that rerun
Protein Structure Tools in Practice
• Running AlphaFold or ColabFold and reading pLDDT and PAE correctly
• Structure visualisation and comparison workflows
• Knowing when a predicted structure is not fit for the intended use
Language Models for Biological Sequence
• Protein language model embeddings and practical downstream uses
• Variant effect estimation from sequence models
• Compute and memory realities of running these tools on modest hardware
LLMs in the Research Workflow
• Code generation for analysis scripts, with verification discipline
• Literature triage and the fabricated-citation problem
• Documenting AI assistance for transparency in publication
Building a Reproducible Project
• Project structure, data versioning and provenance capture
• Benchmarking a tool against a known answer before trusting it
• Sharing an analysis so reviewers and collaborators can rerun it
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Hugging Face |
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