Covers the essentials of data science, including data handling, visualization, and analysis, emphasizing practical skills and active engagement.
Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.
Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.
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.
