Covers the essentials of data science, including data handling, visualization, and analysis, emphasizing practical skills and active engagement.
Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.
Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.
Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.
Covers the basics of machine learning, challenges in deployment, adversarial attacks, and privacy concerns.
Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
Covers the configuration of a project management tool and defining project structures, geographical locations, and risks.
Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.
Covers the basics of machine learning, including supervised and unsupervised techniques, linear regression, and model training.
