Covers the basics of linear regression in machine learning, including model training, loss functions, and evaluation metrics.
Delves into the 'digital turn' in history, examining historical research using digitized newspapers and exploring text reuse, word embeddings, and data visualization.
Covers the fundamentals of data science, emphasizing breadth over depth and practical application in machine learning and data analysis.
Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.
