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DSTC-01574 Online (e-LMS) Graduate / Intermediate

Machine Learning Approaches for Predicting Antimicrobial Resistance (AMR)

by - Dr. Aishwarya Arun Andhare

Master Machine Learning Approaches for Predicting Antimicrobial Resistance (AMR) in 4 weeks through hands-on, project-based online training with DSTC.

★★★★★ Be the first to review • 3 Days · 4.5 hrs • e-Certificate Included
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From ₹2,500 + GST

Programme Parameters

Educational Level:
Graduate / Intermediate
Duration & Workload:
3 Days (4.5 Hrs)
Delivery Mode:
Online (e-LMS)
Prerequisites:
• 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

Real-World Applications Apply Machine Learning Approaches for Predicting Antimicrobial Resistance (AMR) skills directly to academic research, thesis work, and publications. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.

🎯 Program Aim

Real-World Applications
Apply Machine Learning Approaches for Predicting Antimicrobial Resistance (AMR) skills directly to academic research, thesis work, and publications

📋 Course Objectives

1. Apply biotechnology methods to authentic research and industry problems.
2. 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

🚀 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 recordings
🎓

e-Certificate on Completion

Cryptographically verified credential
💬

Post-Programme Support

Direct access to mentors & council
💻

Hands-On Experience

Notebooks, real-world code & datasets

Curriculum Outline

Module 1 Problem

Framing Resistance Prediction

• Predicting phenotype from genotype: the task and its ceiling
• Binary resistance calls versus MIC regression
• Label quality: phenotypic testing error propagating into training data

Module 2 Features

Representing Bacterial Genomes

• Gene presence-absence, k-mer and SNP-based representations
• Pan-genome construction and reference bias
• Population structure as a confounder that inflates cross-validation scores

Module 3 Models

Learning and Validation

• Regularised models and tree ensembles on high-dimensional genomic features
• Phylogeny-aware cross-validation to avoid leakage through relatedness
• Handling severe class imbalance for rare resistance phenotypes

Module 4 Interpretation

Recovering Mechanism

• Feature attribution to known resistance determinants as a sanity check
• Discovering candidate novel determinants and validating them
• Distinguishing mechanism from lineage marker

Module 5 Translation

Clinical and Surveillance Use

• Turnaround time and the clinical decision the prediction must beat
• Regulatory expectations for genomic AST prediction
• Monitoring model performance as resistance mechanisms evolve

Technical Specifications

ParameterRequirement
Covered Tool / PlatformPython
Covered Tool / PlatformScikit-learn
Covered Tool / PlatformTensorFlow
Covered Tool / PlatformKeras
Covered Tool / PlatformPandas
Covered Tool / PlatformNumPy
Covered Tool / PlatformMatplotlib
Covered Tool / PlatformXGBoost

Programme Faculty & Mentors

A
Dr. Aishwarya Arun Andhare Research Fellow Parul University, Gujarat
🔬 Microbiology View Research Profile →

Frequently Asked Questions

This is an Recorded Lectures (Self-Paced) course delivered via our e-LMS platform. You will have access to pre-recorded video lectures, reading materials, assignments, quizzes, and hands-on projects that you can complete at your own pace.

Yes! Upon successful completion of all modules, assignments, and assessments, you will receive an e-Certification along with an e-Marksheet from DSTC (DSTC) that you can showcase on your CV and LinkedIn profile.

Learners should have a foundational understanding of Machine Learning concepts. Familiarity with basic tools and programming is recommended.

You will have access to all course materials for the duration of 3 Days (1.5 hours per day). The self-paced format allows you to learn according to your own schedule through our online learning management system.

Yes, dedicated mentor support is available throughout the course. You can reach out for doubt-clearing sessions, project guidance, and career advice related to Machine Learning. Our mentors are industry experts and experienced professionals. Enroll in Machine Learning Approaches for Predicting Antimicrobial Resistance (AMR) today and take the next step in your professional journey. With expert-curated content, practical projects, and industry-recognized certification, this course is your gateway to mastering Machine Learning skills that matter.

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