Crash Course Recording Available All Levels Dr. Omics

Machine Learning Crash Course

Accelerate therapeutic breakthroughs by mastering predictive AI models for molecular targeting. Bridge the gap between computer science and biotechnology to revolutionize digital drug design.

  • 5.0/5
  • English
  • Updated Aug 2026
Machine Learning Crash Course

About this course

In the rapidly evolving landscape of biomedical research, the integration of artificial intelligence is fundamentally changing how new therapeutics are discovered. This comprehensive online crash course, presented by Dr. Omics Edu, is meticulously crafted to unlock the potential of Machine Learning in Drug Design. Participants will dive deep into the intersection of bioinformatics, computational chemistry, and data science, exploring how deep learning neural networks can predict molecular behaviors. Throughout this program, you will transition from understanding core theoretical frameworks to deploying real-world machine learning models that screen vast chemical libraries. By focusing on practical, hands-on datasets, the curriculum demystifies complex algorithms for predicting binding affinity and optimizing pharmacokinetic profiles. Ultimately, this course equips biologists, chemists, and data enthusiasts with the exact computational tools needed to drastically reduce the time and cost traditional drug discovery takes.

What you will learn

The fundamental role of artificial intelligence (AI) and machine learning (ML) pipelines in modern pharmacology.
How to process, clean, and vectorize molecular structures using SMILES strings and chemical fingerprints.
Methods for building and evaluating predictive models for ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiling.
Strategies to execute virtual screening protocols using automated quantitative structure-activity relationship (QSAR) models.
Techniques for leveraging generative AI to design completely novel, target-specific lead compounds.

Skills you will gain

Bioinformatics Python Cheminformatics Deep-Learning QSAR-Modeling Molecular-Docking Screening Feature-Engineering Analytics
Certification

Available

Issued by Dr. Omics

Course curriculum

1 module

  • 1. Fundamentals of Machine Learning for Genomic Data
  • 2. Linear Models and Nearest Neighbors for Pattern Recognition
  • 3. Probabilistic Machine Learning Concepts and Applications
  • 4. Support Vector Machines SVM Theory and Implementation
  • 5. Naïve Bayes Classifier Fundamentals and Bioinformatics Use Cases
  • 6. Decision Trees and Random Forest Interpretable ML Models
  • 7. Logistic Regression for Predictive Analysis in Bioinformatics
  • 8. Clustering Algorithms for Unsupervised Learning
  • 9. Validation Techniques for Machine Learning Models
  • 10. Machine Learning for Biomedical Image Analysis

What you need to start

  • Foundational understanding of basic biological concepts or molecular chemistry.
  • Familiarity with introductory Python programming concepts (highly recommended but not mandatory, as core concepts are reviewed).

Who this course is for

  • Bioinformaticians and Biologists looking to transition into high-demand computational and AI research roles.
  • Medicinal Chemists wanting to leverage predictive data models to complement their traditional wet-lab assays.
  • Data Scientists and Software Engineers eager to apply their algorithmic skills to the field of life sciences and healthcare.
  • Ph.D. Candidates and Postdoctoral Researchers aiming to incorporate advanced machine learning methodologies into their academic theses or publications.
INR

₹5000

₹10000 50% off
USD

$80

$100 20% off

Indian learners pay in INR; international learners are billed in USD.

Enroll for International Students

Paying from outside India? Use this link to complete your payment.

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This course includes
  • Format Recording Available
  • Level All Levels
  • Language English
  • Modules 1
  • Access 3 months
  • Certificate Yes
  • Provider Dr. Omics
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