• 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