Technologies / Artificial Intelligence

Machine Learning in biomedical data analysis with Python

This training focuses on the practical application of scikit-learn and the Python ecosystem in biomedical data analysis. The program covers clinical data classification, research outcome prediction, feature engineering for genomic data, and model validation tailored to medical datasets. Participants will learn model interpretability techniques (SHAP, LIME) that are critical in the context of medical regulations and clinician trust in AI systems.

Why choose this training?

The rapid advancement of machine learning methods is opening new possibilities in biomedical data analysis — from diagnostics to drug discovery. This training focuses on the practical application of scikit-learn and the Python ecosystem in clinical data classification, research outcome prediction, and feature engineering for genomic data. Special emphasis is placed on model interpretability using SHAP and LIME, which is crucial in the regulated medical environment.

After completing the training, participants will be able to: apply scikit-learn for biomedical and clinical data classification, master feature engineering techniques for genomic, proteomic, and clinical data, build predictive models for clinical trial outcomes, and apply cross-validation methods adapted to the specifics of medical datasets. These competencies directly translate into higher efficiency in IT project execution.

This training is particularly valuable for: Data Scientists working with medical data, bioinformaticians and biomedical data analysts, Python developers in the healthcare sector.

What sets our approach apart?

At EITT, we believe the best learning happens through practice. During 3 days of intensive training, participants work on real-world examples and scenarios, ensuring not only theoretical understanding but above all the ability to apply it in practice.

With over 2,500 trainings in our portfolio and a 4.8/5 rating from participants, EITT is a trusted partner in competency development for organizations of all sizes. Our trainers are practitioners with years of experience who share current knowledge and proven solutions.

Looking for training tailored to your team’s needs? Contact us — we’ll prepare a program customized to your requirements.

Benefits

Participants will learn to apply scikit-learn for biomedical and clinical data classification
They will master feature engineering techniques for genomic, proteomic, and clinical data
They will gain the ability to build predictive models for clinical trial outcomes
They will learn cross-validation methods adapted to the specifics of medical datasets
They will be able to interpret ML models using SHAP and LIME in a medical context
They will learn to select evaluation metrics appropriate for diagnostic problems
They will understand regulatory requirements for deploying ML models in clinical settings
They will develop skills in communicating modeling results to clinical teams

Who is this training for?

Data Scientists working with medical data

Bioinformaticians and biomedical data analysts

Python developers in the healthcare sector

Clinical researchers using statistical methods

Data engineers in pharmaceutical companies

Analytics specialists in research organizations

IT consultants in the medical sector

Biomedical graduates interested in ML

Prerequisites

Basic knowledge of Python and numpy/pandas libraries
Familiarity with statistics and machine learning fundamentals
Basic understanding of biomedical or clinical data
Experience working with Jupyter Notebook

Training program

Overview of libraries (scikit-learn, pandas, numpy, biopython)
Loading and exploring biomedical datasets
Medical data standards (FHIR, HL7, DICOM metadata)
Ethics and regulations for working with patient data

Processing genomic and proteomic data
Feature selection with filter and wrapper methods
Dimensionality reduction (PCA, t-SNE, UMAP)
Handling imbalanced data in medical contexts

Classifiers for diagnostics (SVM, Random Forest, Gradient Boosting)
Clinical trial outcome prediction
Logistic regression in epidemiology
Ensemble methods for prediction stability

Cross-validation accounting for patient data structure
Medicine-specific metrics (sensitivity, specificity, AUC-ROC)
Stratification and temporal validation
Statistical testing of model results

SHAP — feature impact analysis on predictions
LIME — local model interpretability
Reporting results for clinicians
Regulatory requirements for ML models in medicine

Delivery Methods

Online

Convenience of participating from anywhere
Interactive live sessions with trainer
Materials available for 30 days
No travel costs

On-site

Direct contact with trainer and group
Intensive hands-on workshops
Networking with other participants
Full focus on learning

Frequently asked questions

What are the prerequisites for this training?

For Machine Learning in biomedical data analysis with Python we recommend: Basic knowledge of Python and numpy/pandas libraries; Familiarity with statistics and machine learning fundamentals; Basic understanding of biomedical or clinical data.

What is the format and duration of this training?

The training lasts 3 days and is available in online and on-site format. Sessions run from 9:00 AM to 4:00 PM. We can also customize the schedule to fit your team's needs.

Who is this training designed for?

This training is designed for: Data Scientists working with medical data; Bioinformaticians and biomedical data analysts; Python developers in the healthcare sector.