دانلود کتاب یادگیری ماشین کاربردی

Machine learning (ML) is one of the most exciting fields of computing today. Over the past few decades, ML has become an entrenched part of everyday life and has been successfully used to solve practical problems. An application area of ML is very broad, including engineering, industry, business, finance, medicine, and many other domains. ML covers a wide range of learning algorithms, including classical ones such as linear regression, k-nearest neighbors, decision trees, support vector machines and neural networks, and newly developed algorithms such as deep learning and boosted tree models. In practice, it is quite challenging to properly determine an appropriate architecture and parameters of ML models so that the resulting learner model can achieve sound performance for both learning and generalization. Practical applications of ML bring additional challenges, such as dealing with big, missing, distorted, and uncertain data. In addition, interpretability is a paramount quality that ML methods should aim to achieve if they are to be applied in practice. Interpretability allows us to understand ML model operation and raises confidence in its results. This book compiles 41 papers published in the Special Issue titled “Applied Machine Learning”. The papers focus on applications of ML models in a diverse range of fields and problems. They report substantive results on a wide range of learning methods, discuss conceptualization of problems, data representation, feature engineering, ML models, critical comparisons with existing techniques, and interpretation of results.