• 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

For this lab, we will use Google Colab for exploring pandas and seaborn. 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.

Classification Dataset

Regression dataset