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PREFACE
The provided text is a comprehensive guide that discusses various aspects of machine learning algorithms and their applications. It starts by introducing the three main types of machine learning: unsupervised learning, supervised learning, and semi-supervised learning.
The guide then delves into dimensionality reduction, which is useful when dealing with large datasets or numerous features. It explains the concept of dimension reduction algorithms and discusses three popular algorithms: Principal Component Analysis (PCA), Singular Value Decomposition (SVD), and Linear Discriminant Analysis (LDA). Each algorithm is explained in terms of its purpose, applications, and suitability for different types of data.
The text also covers clustering techniques, which group similar data points together based on their intrinsic characteristics. It mentions the DBSCAN algorithm for clustering non-labeled data and provides insights into three commonly used algorithms for clustering with a specified number of clusters: K-Modes, K-Means, and Gaussian Mixture Model (GMM).
The guide then shifts focus to regression, a powerful machine learning algorithm used for predicting continuous numerical values as outcomes. It explains linear regression and its applications in various fields. It also discusses other regression algorithms, including Neural…
