Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
Covers logistic regression for linear classification and unsupervised dimensionality reduction techniques.
Covers the key concepts of PCA, including reducing data dimensionality and extracting features, with practical exercises.
Covers data representation using PCA for dimensionality reduction, focusing on signal preservation and noise removal.
Covers data streams, sub-linear memory computation, document similarity, and randomized dimension reduction techniques for handling 'Big Data' challenges efficiently.
