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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


In this lab, we will study the basic performance evaluation in machine learning, covering elementary concepts such as classification, regression, data fitting, clustering and much more.

You will work in an environment that is easy to use, and provides a couple of tools like manipulating data and visualizing results. We will use Google Colab, which comes with a variety of useful tools already installed.

You can also check out these cheet sheets for fast reference to the common libraries:

Cheat sheets:

This lab is organized in a Jupyer Notebook hosted on Google Colab. You will find there some intuitions and applications for pandas and seaborn. Check out the Tasks section below.

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 โ€œOpen in Colaboratoryโ€.

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

ep/labs/10.1638628212.txt.gz ยท Last modified: 2021/12/04 16:30 by vlad.stefanescu
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