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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


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

All tasks are tutorial based and every exercise will be associated with at least one “TODO” within the code. Those tasks can be found in the exercises package, but our recommendation is to follow the entire skeleton code for a better understanding of the concepts presented in this laboratory class. Each functionality is properly documented and for some exercises, there are also hints placed in the code.

Because the various tasks and exercises are spread throughout the laboratory text, they are marked with a ⚠️ emoji. Make sure you look for this emoji so that you don't miss any of them!


ep/labs/10.1638627380.txt.gz · Last modified: 2021/12/04 16:16 by vlad.stefanescu
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