• Advanced Courses in Life Sciences

    Header of Systems Biology at Transmitting Science

Online Course – 5th Edition

Python Machine Learning in Biology

Python Machine Learning in Biology


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Course Overview & Programme

The field of biological sciences is becoming increasingly information-intensive and data-rich. For example, the growing availability of DNA sequence data or clinical measurements from humans promises a better understanding of the important questions in biology. However, the complexity and high-dimensionality of these biological data make it difficult to pull out mechanisms from the data. Machine learning techniques promise to be useful tools for resolving such questions in biology because they provide a mathematical framework to analyze complex and vast biological data. In turn, the unique computational and mathematical challenges posed by biological data may ultimately advance the field of machine learning as well.

This course will cover basics of the Python programming language as well as the pandas and sklearn Python libraries for data wrangling and machine learning.

By the end of this course, participants will understand:

  • How to input and clean data in Python using the pandas library
  • How to perform exploratory data analysis in Python
  • How to use the sklearn library in Python for machine learning workflows
  • How to choose an appropriate machine learning model for the task
  • How to use supervised machine learning models (SVM, Decision Trees, Neural Networks, etc.) for classification tasks
  • How to use unsupervised machine learning models for clustering tasks
  • How to evaluate machine learning models and interpret their results

This course is intended to give participants a conceptual overview of machine learning algorithms and an intuition for the mathematics underlying them, equipping participants to be able to choose and implement appropriate models for biological datasets.

Day 1

Python Foundations

  • Morning: Python Basics, Handling Data in Pandas, Basic Pandas Data Cleaning
  • Afternoon: Exploratory Data Analysis in Pandas, Data Visualization in Python.

Day 2

Supervised Machine Learning: Classification

  • Morning: KNN, Introduction to sklearn workflow.
  • Afternoon: Train/Test Split, and Bias-Variance Tradeoff, Model Evaluation.

Day 3

Supervised Machine Learning: Classification

  • Morning: Decision Trees and Random Forest
  • Afternoon: Support Vector Machines

Day 4

Unsupervised Machine Learning

  • Morning: Clustering Methods (K Means Clustering)
  • Afternoon: Advanced Clustering Methods Hierarchical Clustering, DBSCAN

Day 5

  • Special Topics
  • Participants will have the option to learn a particular model or receive an introduction to Neural Networks theory and applications.

Graduate or postgraduate degree in Life Sciences and basic knowledge of Statistics. While some Python knowledge is useful, the course will cover basic Python skills necessary to input, clean, and explore data as well as build and evaluate machine learning models.

All participants must use their own computer (Windows, Macintosh, Linux), with access to a good internet connection.


Nichole Bennett instructor fro Transmitting Science

Nic Bennett
The University of Texas at Austin
United States of America

Jo Villa Instructor for Transmitting Science

Dr. Jo Villa
KOTAI Biotechnologies, Inc.

Dates & Schedule

November 16th-December 14th, 2023

13:00-16:30 (Madrid time zone)

Weekly online live sessions on Thursdays:

16, 23, 30 November
7, 14 December

Total course hours: 35

17.5 hours of online live lessons, plus 17.5 hours of pre-recorded classes and supervised assignments.

This course is equivalent to 1 ECTS (European Credit Transfer System) at the Life Science Zurich Graduate School.

The recognition of ECTS by other institutions depends on each university or school.



This course will be delivered live online

This course will be taught using a combination of live (synchronous) sessions on Zoom, pre-recorded lectures, and tasks to be completed in between live sessions on the Slack platform.

Live sessions will be recorded. Recordings will be made available to participants for a limited period of time. However, attendance to the live sessions is required.


Places are limited to 18 participants and will be occupied by strict registration order.

Participants who have completed the course will receive a certificate.

Haris Saslis instructor for Transmitting Science

Dr. Haris Saslis
Transmitting Science

Fees & Discounts

  • Course Fee
  • Early bird (until September 30th, 2023):
  • 556 €
    (444.80 € for Ambassador Institutions)
  • Regular (after September 30th, 2023):
  • 640 €
    (512 € for Ambassador Institutions)
  • Prices include VAT.
    After registration you will receive confirmation of your acceptance on the course.
    Payment is not required during registration.

We offer discounts on the Course Fee.

Discounts are not cumulative. Participants receive the highest appropriate discount.

We also offer the possibility of paying in two instalments. Please contact us to request this.

Former participants of Transmitting Science courses receive a 5% discount on the Course Fee.

20% discount on the Course Fee is offered to members of certain organisations (Ambassador Institutions). If you wish to apply for this discount, please indicate it in the Registration form (proof will be asked later). If you would like your institution to become a Transmitting Science Ambassador Institution, please contact us at communication@transmittingscience.com

Unemployed scientists, as well as PhD students without any grant or scholarship to develop their PhD, can benefit from a 40% discount on the Course Fee. This applies only to participants based in Spain. If you wish to ask for this discount, please contact us. The discount may apply for a maximum of 2 places, which will be covered by strict registration order.