BIO HELP LEARNING COURSE

AI and Machine Learning for Bioinformatics

Apply machine learning to omics data: feature selection, classification, clustering, model validation and an introduction to deep learning with scikit-learn and PyTorch.

Programming

◉ Advanced◷ 15 Hours▣ Certificate Included

Course Information

Everything you need to know before enrolling.

LANGUAGEEnglish

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Registration for the next live batch opens soon. Pre-register and our team will contact you with the batch dates, fees and registration link as soon as it opens.

Live online classes
Group & college batches
Certificate on completion

Pre-register for live training → Ask on WhatsApp Or email [email protected]

▤ Overview ☷ Curriculum ♙ Instructor ☆ Reviews ▣ FAQs

▤

About This Course

Machine learning is now part of many genomics and drug discovery projects, but it is easy to build models that look good and fail on new data. This course teaches how to apply ML to biological data correctly.

You prepare omics datasets, reduce dimensions, cluster samples, train and tune classifiers, validate them properly, interpret which features matter and finish with an introduction to deep learning for biological sequences.

Who this course is for

Bioinformatics learners who know basic Python
Researchers who want to build predictive models from omics data
Students exploring AI careers in life sciences

Prerequisites

Python for Biologists (or equivalent Python) and basic statistics.

Final project

Train, validate and interpret a classifier that predicts sample groups from a public omics dataset.

◎

What You’ll Learn

✓Prepare omics data for machine learning

✓Use PCA, t-SNE and UMAP to explore data

✓Cluster samples and genes

✓Train classifiers including random forests and SVMs

✓Validate models with cross-validation and proper test sets

✓Interpret models with feature importance and SHAP

✓Build a simple deep learning model for sequences

⌘

Tools & Technologies

PythonJupyter NotebookpandasNumPyscikit-learnXGBoostSHAPPyTorchMatplotlibseaborn

Course Curriculum

What you will learn, module by module

10 modules 15 hours of training 40 topics Certificate on completion

- Where ML helps and where it misleads
- Supervised vs. unsupervised learning
- Typical omics ML problems
- Setting up the Python environment
- Normalization and scaling
- Handling missing values
- Train, validation and test splits
- Avoiding data leakage
- PCA for omics data
- t-SNE and UMAP
- Interpreting embeddings
- Choosing components
- k-means and hierarchical clustering
- Choosing the number of clusters
- Evaluating clusters
- Clustering samples and genes
- Logistic regression
- Random forests and gradient boosting
- Support vector machines
- Class imbalance
- Cross-validation
- Hyperparameter tuning
- ROC, AUC, precision and recall
- Reporting results honestly
- Feature importance
- SHAP values
- Selecting biomarker candidates
- Biological interpretation of models
- Regression models for continuous traits
- Regularization: lasso and ridge
- Evaluating regression models
- Case study
- Neural networks explained
- Building a model in PyTorch
- One-hot encoding DNA sequences
- A simple sequence classifier
- End-to-end ML on a public omics dataset
- Validation and interpretation
- Writing up the model
- Presentation and feedback

♙

Your Instructor

Bio Help Learning Educator

Bioinformatics Educator & Mentor

Learn from experienced educators and practitioners through practical, career-focused bioinformatics training.

Frequently Asked Questions

6 answers about this course

Do I need strong mathematics?

No. Concepts are explained visually and practically; basic statistics is enough.

Is this course in Python or R?

Python, using scikit-learn and PyTorch.

Does this course cover large language models?

It introduces deep learning foundations. Large language models are mentioned in context but are not the focus.

Will I get a certificate?

Yes, after completing all modules, the quizzes and the final project.

How do I get fee and batch details?

Pre-register on this page or contact us on WhatsApp, and our team will share the details.

How long will I have access to the course?

You get full access to all course materials for 3 months from the date of enrollment.

Still have a question?Our team usually replies within one working day.

Ask on WhatsApp →

WHAT OUR LEARNERS SAY

Real Stories. Real Growth.

Hear from learners building practical skills with Bio Help Learning.

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As i am from biotechnology background this workshop, actually very helpfull for me

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Learn about different molecular docking tools.

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The workshop was informative and provided valuable insights into bioinformatics tools and techniques.

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The Drug Discovery Workshop was highly informative and helped me gain valuable knowledge about the drug discovery process, including molecular docking, drug–target interactions, and computational tools used in drug design.

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