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Muller M: Data Science

Data Science

Engineering Design with SOLIDWORKS 2025 provides a solid foundation in SOLIDWORKS by using projects with step-by-step instructions that are perfect for both beginners and intermediate users.

A Step-by-Step Project Based Approach Utilizing 3D Solid Modeling

Engineering Design with SOLIDWORKS 2025

Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

Written by leaders in the data mining community, including the developers of the RapidMiner software, this book provides an in-depth introduction to the application of data mining and business analytics techniques and tools in scientific research, medicine, industry, commerce, and diverse other sectors. It presents the most powerful and flexible open source software solutions: RapidMiner and RapidAnalytics. The new edition will update all chapters to RapidMiner 7, and will add at least six new chapters, including new chapters on text mining, time series, and educational data mining.

Data Mining Use Cases and Business Analytics Applications

RapidMiner, Second Edition

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: <ul><li>A blend of robust statistics and machine learning principles</li><li>Detailed discussion of a wide range of robust machine learning methodologies, from robust clustering, regression and classification, to neural networks and anomaly detection</li><li>Python code with immediate application to data science problems</li></ul> 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.

Handling Outliers and Anomalies in Data Science

Fundamentals of Robust Machine Learning

Numerical Methods-Based Approaches

Fatigue Damage in Metals

Wiley Series in Corrosion

Corrosion of Nickel-Based Alloys

This book focuses on piezoelectric toughening, where a part of the mechanical energy inducing crack extension is converted into electrical energy or dissipated by domain alignment/domain wall movement. The effectiveness of this toughening mechanism, like other established mechanisms, depends on several aspects such as energetics of the piezoelectric transition, processing/densification of the matrix phase, particle size and distribution of secondary phase, interface between secondary and matrix phase etc. Therefore, to develop a comprehensive understanding, knowledge of all the aspects is a prerequisite. This book is therefore dedicated to all aspects of toughening by piezoelectric secondary phases.

Piezoelectric Toughening of Ceramics

Discover the AI and data skills you'll need and develop your confidence to succeed in the digital age.

Gain the Confidence You Need to Succeed

Data and AI Skills

Specifically written for those who are new to SOLIDWORKS, A Hands-On Introduction to SOLIDWORKS 2025 allows you to learn as you follow an expert in SOLIDWORKS through the basics of the software to its more in-depth capabilities. This book works perfectly for a freshman design class or as a companion text to an engineering graphics textbook.

Text and Video Instruction

A Hands-On Introduction to SOLIDWORKS 2025

Fundamentals of Practical Reliability Engineering

Woodhead Publishing Series in Welding and Other Joining Technologies

Mechanical Joining by Material Flow provides sought-after experimental information on process design, process modeling, and joint characterization techniques for students, researchers, and engineering professionals who want to learn about advanced joining systems for lightweight materials. Chapters cover mechanical joining by plastic deformation, including clinching and self-piercing riveting, and friction-based processes, such as friction stir welding to form a mechanical joint. Though these processes are mainly used to establish metallurgical bonding, their unique characteristics and the material flow they generate has led to an increased use of these techniques for joining difficult-to-weld materials. Other sections introduce innovative technologies developed based on material flow to meet existing industry needs to join materials with different structural properties in different scales or shapes. To furnish readers with a more usefully comprehensive coverage, the team of experts behind the book has included methodological principles as well as discussions on advantages, drawbacks, and future directions of each technique, alongside application examples and case studies.

Mechanical Joining by Material Flow

Handbook of Energy Materials in Supercapacitors and Storage Devices

Digital twins represent an emerging technology of immense potential across various industries. Their significance is particularly pronounced within Industry 4.0 and smart manufacturing paradigms, which strive to elevate efficiency and quality through seamless digital integration. By amassing and scrutinizing extensive data streams, digital twins empower data-centric decision-making-a pivotal asset in contemporary industry. Digital Twins of Advanced Materials Processing bridges the gap in comprehensive resources concerning advanced materials processing, a domain characterized by rapid evolution. It provides pragmatic remedies and real-world case studies, catering to tangible implementation needs. Moreover, digital twins hold the capacity to amplify efficiency and innovation within materials processing-a perspective deeply explored within this book, rendering it invaluable for professionals, researchers, and students alike. The prospects of employing digital twins in materials processing span diverse horizons: refining materials innovation, streamlining processes, enabling data-driven maintenance, enhancing product quality, and unearthing insights rooted in data. The book also undertakes the challenge of addressing key issues encompassing data amalgamation and integrity, model validation and calibration, software and data safeguarding, scalability, and cost considerations.

Digital Twins of Advanced Materials Processing

Data Science

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