Embark on a Python-based machine learning project focused on classifying EEG time-series data from a sleep study. Learn to differentiate between awake and asleep states using 30-second EEG segments and random forest classification. Explore key concepts including the EDF data format, power spectral density, and the classification scheme. Follow along with a step-by-step guide covering code setup, data loading and pre-processing, feature engineering, and implementation of the random forest classifier. Gain practical insights into working with EEG data and applying machine learning techniques to real-world neuroscience problems.