Machine Learning (ML) has become an important tool for analyzing complex brain activities, such as Electroencephalography (EEG). EEG signals are complex, noisy and high-dimensional, making them difficult to analyze manually (Campos et al. in Brain Sci 14:894, 2024). ML algorithms can identify subtle patterns in EEG data, making them suitable for various applications, such as Brain-Computer Interface (BCI) and Epilepsy detection (Sanei and Chambers JA in EEG signal processing. Wiley, 2013). The ML process involves three major steps, which include: (i) preprocessing, (ii) feature extraction and (iii) classification/regression. This chapter explains the use of ML and EEG in Python, starting with normalization, followed by ML features, and then regression and classification methods.