This is an old revision of the document!

Lab 10 - Machine Learning


  • Understand basic concepts of machine learning
  • Remember examples of real-world problems that can be solved with machine learning
  • Learn the most common performance evaluation metrics for machine learning models
  • Analyse the behaviour of typical machine learning algorithms using the most popular techniques
  • Be able to compare multiple machine learning models


The exercises will be solved in Python, using various popular libraries that are usually integrated in machine learning projects:

  • Scikit-Learn: fast model development, performance metrics, pipelines, dataset splitting
  • Pandas: data frames, csv parser, data analysis
  • NumPy: scientific computation
  • Matplotlib: data plotting

Classification Dataset Regression dataset


Google Colab Notebook

For this lab, we will use Google Colab for exploring performance evaluation in machine learning. Please solve your tasks here by clicking “Open in Colaboratory”.

You can then export this python notebook as a PDF (File → Print) and upload it to Moodle.


ep/labs/10.1638627879.txt.gz · Last modified: 2021/12/04 16:24 by vlad.stefanescu
CC Attribution-Share Alike 3.0 Unported Valid CSS Driven by DokuWiki do yourself a favour and use a real browser - get firefox!! Recent changes RSS feed Valid XHTML 1.0