Machine Learning For Science And Engineering Volume 1 Fundamentals
Download Machine Learning For Science And Engineering Volume 1 Fundamentals PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Machine Learning For Science And Engineering Volume 1 Fundamentals book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Machine Learning for Science and Engineering, Volume 1: Fundamentals
This textbook teaches underlying mathematics, terminology, and programmatic skills to implement, test, and apply machine learning to real-world problems. Exercises with field data, including well logs and weather measurements, prepare and encourage readers to begin using software to validate results and program their own creative data solutions. As the size and complexity of data soars exponentially, machine learning (ML) has gained prominence in applications in geoscience and related fields. ML-powered technology increasingly rivals or surpasses human performance and fuels a large range of leading-edge research. This textbook teaches the underlying mathematics, terminology, and programmatic skills to implement, test, and apply ML to real-world problems. It builds the mathematical pillars required to thoroughly comprehend and master modern ML concepts and translates the newly gained mathematical understanding into better applied data science. Exercises with raw field data, including well logs and weather measurements, prepare and encourage the reader to begin using software to validate results and program their own creative data solutions. Most importantly, the reader always keeps an eye on the ML’s imperfect data situations as encountered in the real world.
Applied Machine Learning for Data Science Practitioners
Author: Vidya Subramanian
language: en
Publisher: John Wiley & Sons
Release Date: 2025-04-01
A single-volume reference on data science techniques for evaluating and solving business problems using Applied Machine Learning (ML). Applied Machine Learning for Data Science Practitioners offers a practical, step-by-step guide to building end-to-end ML solutions for real-world business challenges, empowering data science practitioners to make informed decisions and select the right techniques for any use case. Unlike many data science books that focus on popular algorithms and coding, this book takes a holistic approach. It equips you with the knowledge to evaluate a range of techniques and algorithms. The book balances theoretical concepts with practical examples to illustrate key concepts, derive insights, and demonstrate applications. In addition to code snippets and reviewing output, the book provides guidance on interpreting results. This book is an essential resource if you are looking to elevate your understanding of ML and your technical capabilities, combining theoretical and practical coding examples. A basic understanding of using data to solve business problems, high school-level math and statistics, and basic Python coding skills are assumed. Written by a recognized data science expert, Applied Machine Learning for Data Science Practitioners covers essential topics, including: Data Science Fundamentals that provide you with an overview of core concepts, laying the foundation for understanding ML. Data Preparation covers the process of framing ML problems and preparing data and features for modeling. ML Problem Solving introduces you to a range of ML algorithms, including Regression, Classification, Ranking, Clustering, Patterns, Time Series, and Anomaly Detection. Model Optimization explores frameworks, decision trees, and ensemble methods to enhance performance and guide the selection of the most effective model. ML Ethics addresses ethical considerations, including fairness, accountability, transparency, and ethics. Model Deployment and Monitoring focuses on production deployment, performance monitoring, and adapting to model drift.
Smart Cyber-Physical Power Systems, Volume 1
Authoritative, highly comprehensive guide on how emerging technologies can address various challenges in different sectors of smart cyber-physical power systems As the world shifts towards smarter and more resilient energy systems, cyber-physical power systems (CPSs) represent a critical step in modernizing the power infrastructure. Smart Cyber-Physical Power Systems, Volume 1: Fundamental Concepts, Challenges, and Solutions, offers an in-depth exploration of the fundamental concepts, structures, and major challenges that underlie these complex systems. It covers the essential theories and frameworks that drive the integration of digital technologies with physical power systems, including smart grids, microgrids, and the Internet of Energy. This volume addresses a range of crucial topics, from global demand response strategies and microgrid architectures to smart energy management in cities and advanced distributed control strategies. Additionally, it highlights key challenges such as ensuring resiliency, protecting against cyberattacks, and maintaining reliability in the face of rapid technological advancements. Experts from around the world contribute to this volume, sharing vital insights into the transformation of traditional power systems into adaptive, cyber-physical networks. Their focus on the growing importance of privacy, security, and data analytics makes this book a critical resource for anyone involved in power system research, offering essential tools to navigate and shape the future landscapes of energy systems. Whether you’re a researcher, engineer, or industry professional, this volume provides the foundational knowledge needed to understand the evolving landscape of smart cyber-physical power systems and the significant challenges they face. Join us on a journey through the landscape of Smart Cyber-Physical Power Systems (CPPSs), where cutting-edge solutions meet the challenges of today and forge the energy paradigms of tomorrow, driven by AI/ML, Big Data, Blockchain, IoT, Quantum Computing, Information Theory, Edge Computing, Metaverse, DevOps, and more.