Machine learning: practical

Trainers: Melissa Adasme, Jiawei Wang

Overview: Building on the concepts, this session transitions to practical application using Python. You will actively participate in a guided, step-by-step walkthrough of a typical machine learning workflow using a relevant biological dataset. We’ll utilize standard data science libraries, focusing on understanding and implementing each stage: loading and preparing data, training a basic model, making predictions, and evaluating the results.

Learning outcomes

By the end of this session, you will be able to:

• Apply a basic machine learning pipeline in Python to biological data

Materials

• Hands-on session guidelines
• Machine learning: practical

The link will redirect you to a Google Colab file.

Never used Google Colab before?

Google Colab is a free cloud-based service that allows you to write and execute Python code in your web browser, similar to what Google Docs allows for writing. To run and modify the code provided here, you can:

• Download the Colab file as a .ipynb file, and open it with Jupyter Notebook. You can then work on the code on your own machine. Please note that by doing so, you will have to independently manage the code dependencies.
• Create your own private copy of the Colab in your Drive

You can find more information on Jupyter Notebook and how to install it on the “Installing Jupyter” page of their website. Please note that if you choose to download the file, you will have to independently manage the code dependencies on your machine.