9781394294374 - Fundamentals of Robust Machine Learning: Handling Outliers and Anomalies in Data Science di Saleh, Resve A.; Majzoub, Sohaib; Saleh, A. K. Ehsanes (19 risultati)

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Hardcover. Condizione: new. Hardcover. An essential guide for tackling outliers and anomalies in machine learning and data science. In recent years, machine learning (ML) has transformed virtually every area of research and technology, becoming one of the key tools for data scientists. Robust machine learning is a new approach to handling outliers in datasets, which is an often-overlooked aspect of data science. Ignoring outliers can lead to bad business decisions, wrong medical diagnoses, reaching the wrong conclusions or incorrectly assessing feature importance, just to name a few. Fundamentals of Robust Machine Learning offers a thorough but accessible overview of this subject by focusing on how to properly handle outliers and anomalies in datasets. There are two main approaches described in the book: using outlier-tolerant ML tools, or removing outliers before using conventional tools. Balancing theoretical foundations with practical Python code, it provides all the necessary skills to enhance the accuracy, stability and reliability of ML models. Fundamentals of Robust Machine Learning readers will also find: A blend of robust statistics and machine learning principlesDetailed discussion of a wide range of robust machine learning methodologies, from robust clustering, regression and classification, to neural networks and anomaly detectionPython code with immediate application to data science problems Fundamentals of Robust Machine Learning is ideal for undergraduate or graduate students in data science, machine learning, and related fields, as well as for professionals in the field looking to enhance their understanding of building models in the presence of outliers. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Hardcover. Condizione: new. Hardcover. An essential guide for tackling outliers and anomalies in machine learning and data science. In recent years, machine learning (ML) has transformed virtually every area of research and technology, becoming one of the key tools for data scientists. Robust machine learning is a new approach to handling outliers in datasets, which is an often-overlooked aspect of data science. Ignoring outliers can lead to bad business decisions, wrong medical diagnoses, reaching the wrong conclusions or incorrectly assessing feature importance, just to name a few. Fundamentals of Robust Machine Learning offers a thorough but accessible overview of this subject by focusing on how to properly handle outliers and anomalies in datasets. There are two main approaches described in the book: using outlier-tolerant ML tools, or removing outliers before using conventional tools. Balancing theoretical foundations with practical Python code, it provides all the necessary skills to enhance the accuracy, stability and reliability of ML models. Fundamentals of Robust Machine Learning readers will also find: A blend of robust statistics and machine learning principlesDetailed discussion of a wide range of robust machine learning methodologies, from robust clustering, regression and classification, to neural networks and anomaly detectionPython code with immediate application to data science problems Fundamentals of Robust Machine Learning is ideal for undergraduate or graduate students in data science, machine learning, and related fields, as well as for professionals in the field looking to enhance their understanding of building models in the presence of outliers. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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Hardcover. Condizione: new. Hardcover. An essential guide for tackling outliers and anomalies in machine learning and data science. In recent years, machine learning (ML) has transformed virtually every area of research and technology, becoming one of the key tools for data scientists. Robust machine learning is a new approach to handling outliers in datasets, which is an often-overlooked aspect of data science. Ignoring outliers can lead to bad business decisions, wrong medical diagnoses, reaching the wrong conclusions or incorrectly assessing feature importance, just to name a few. Fundamentals of Robust Machine Learning offers a thorough but accessible overview of this subject by focusing on how to properly handle outliers and anomalies in datasets. There are two main approaches described in the book: using outlier-tolerant ML tools, or removing outliers before using conventional tools. Balancing theoretical foundations with practical Python code, it provides all the necessary skills to enhance the accuracy, stability and reliability of ML models. Fundamentals of Robust Machine Learning readers will also find: A blend of robust statistics and machine learning principlesDetailed discussion of a wide range of robust machine learning methodologies, from robust clustering, regression and classification, to neural networks and anomaly detectionPython code with immediate application to data science problems Fundamentals of Robust Machine Learning is ideal for undergraduate or graduate students in data science, machine learning, and related fields, as well as for professionals in the field looking to enhance their understanding of building models in the presence of outliers. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.