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Total Size:
10.1 MB
Info Hash:
83F9371E37ED3FC1B9C9486DF7AA22CBCD711A3E
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Added:
April 20, 2026, 1:27 p.m.
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(Last updated: April 20, 2026, 1:32 p.m.)
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| Saleh R. Fundamentals of Robust Machine Learning...in Data Science 2025.pdf | 10.1 MB |
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NOTE
SOURCE: Saleh R. Fundamentals of Robust Machine Learning...in Data Science 2025
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COVER
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MEDIAINFO
Textbook in PDF format 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 principles • Detailed discussion of a wide range of robust Machine Learning methodologies, from robust clustering, regression and classification, to neural networks and anomaly detection • Python 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. Preface Introduction Robust Linear Regression The Log-Cosh Loss Function Outlier Detection, Metrics, and Standardization Robustness of Penalty Estimators Robust Regularized Models Quantile Regression Using Log-Cosh Robust Binary Classification Neural Networks Using Log-Cosh Multi-class Classification and Adam Optimization Anomaly Detection and Evaluation Metrics Case Studies in Data Science
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