Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.
Covers clustering, semi-supervised clustering, and binary classification formalization, along with various classification techniques.
Covers clustering and the K-means algorithm for partitioning datasets into clusters based on similarity.
Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.
Covers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.
Covers the basics of machine learning, challenges in deployment, adversarial attacks, and privacy concerns.
