MLG 012 Shallow Algos 1

This podcast episode explores the importance of shallow learning algorithms as foundational knowledge for machine learning engineers. It highlights the limitations of deep learning and the benefits of using shallow learning algorithms for specific problem types. The episode provides an overview of commonly used shallow learning algorithms, emphasizes the need for a comprehensive understanding of these approaches, and discusses the hierarchical breakdown of machine learning into supervised, unsupervised, reinforcement learning, and semi-supervised learning. The episode also introduces specific algorithms such as K Nearest Neighbors, K-means, Apriori, Principal Component Analysis, Decision Trees, and explores topics such as clustering, association rule learning, and dimensionality reduction. It concludes with recommendations for further resources to deepen understanding of shallow learning algorithms and the theory behind machine learning.