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.
