"Hands-On Implementation of Machine Learning Models" is a practical guide designed to bridge the gap between machine learning theory and real-world applications. It provides clear explanations of fundamental and advanced machine learning models, including linear and logistic regression, decision trees, support vector machines, k-means clustering, PCA, hierarchical clustering, random forests, gradient boosting machines, and neural networks.

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