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Why Walmart Pre-Owned?

for additional details & specific condition qualifications.

Walmart Pre-Owned allows you to find previously owned, well-cared-for items from Walmart’s trusted & performance-managed sellers. Shopping Pre-Owned allows you to bring home the best-quality picks at even lower prices, in addition to extending the life of an item & reducing waste. Find your favorites & shop a range of conditions in every category.

Each Pre-Owned item listed comes from Walmart’s trusted performance-managed sellers, to ensure you get quality items.

Save even more on top brands & your most-loved items.

Don’t love it? Most items offer a 30-day* free return policy, for added peace of mind.

Shopping Pre-Owned helps in extending the life of an item & reducing waste.

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Prices, terms, and availability may vary online, in stores, and in-app. Items ordered online may be available for pick up at store, shipping, or delivery. Some items may be available from Marketplace Sellers, who set their own prices, and are not eligible for price match. Items sold online by Walmart may be eligible for price match. See our

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Save up to 40% on additional pairs

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Purchase one complete pair of eyeglasses (frame and prescription lenses) and get 40% off additional pairs. Offer valid July 6–September 1, 2026, while supplies last. No rain checks. Additional pairs must be purchased for the same patient, on the same day, in the same transaction, and must be of equal or lesser value than the highest-priced pair. Exclusions apply, including prescription sunglasses, Ray-Ban Meta, Oakley Meta, and Nuance Audio. OR Purchase a complete pair of prescription sunglasses and get 35% off each additional lower-priced complete pair of prescription glasses. Discount applies to the lowest-priced pairs. Offers cannot be combined with other promotions and do not apply to insurance purchases, managed care, third-party payor programs, or state and federal healthcare programs. Valid prescription required. Eye exam not included

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Mastering Java Machine Learning: A Java developer's guide to implementing machine learning and big data architectures, (Paperback)

<ul> <li>Mastering Java Machine Learning: A Java developer's guide to implementing machine learning and big data architectures, (Paperback)</li> <li>Author: Packt Publishing</li> <li>ISBN: 9781785880513</li> <li>Format: Paperback</li> <li>Publication Date: 2017-06-30</li> <li>Page Count: 556</li> </ul>

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Become an advanced practitioner with this progressive set of master classes on application-oriented machine learning Key Features <ul> <li>Comprehensive coverage of key topics in machine learning with an emphasis on both the theoretical and practical aspects</li> <li>More than 15 open source Java tools in a wide range of techniques, with code and practical usage.</li> <li>More than 10 real-world case studies in machine learning highlighting techniques ranging from data ingestion up to analyzing the results of experiments, all preparing the user for the practical, real-world use of tools and data analysis.</li> </ul> Book Description Java is one of the main languages used by practicing data scientists; much of the Hadoop ecosystem is Java-based, and it is certainly the language that most production systems in Data Science are written in. If you know Java, Mastering Machine Learning with Java is your next step on the path to becoming an advanced practitioner in Data Science. This book aims to introduce you to an array of advanced techniques in machine learning, including classification, clustering, anomaly detection, stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, deep learning, and big data batch and stream machine learning. Accompanying each chapter are illustrative examples and real-world case studies that show how to apply the newly learned techniques using sound methodologies and the best Java-based tools available today. On completing this book, you will have an understanding of the tools and techniques for building powerful machine learning models to solve data science problems in just about any domain. What you will learn <ul> <li>Master key Java machine learning libraries, and what kind of problem each can solve, with theory and practical guidance.</li> <li>Explore powerful techniques in each major category of machine learning such as classification, clustering, anomaly detection, graph modeling, and text mining.</li> <li>Apply machine learning to real-world data with methodologies, processes, applications, and analysis.</li> <li>Techniques and experiments developed around the latest specializations in machine learning, such as deep learning, stream data mining, and active and semi-supervised learning.</li> <li>Build high-performing, real-time, adaptive predictive models for batch- and stream-based big data learning using the latest tools and methodologies.</li> <li>Get a deeper understanding of technologies leading towards a more powerful AI applicable in various domains such as Security, Financial Crime, Internet of Things, social networking, and so on.</li> </ul> Who this book is for This book will appeal to anyone with a serious interest in topics in Data Science or those already working in related areas: ideally, intermediate-level data analysts and data scientists with experience in Java. Preferably, you will have experience with the fundamentals of machine learning and now have a desire to explore the area further, are up to grappling with the mathematical complexities of its algorithms, and you wish to learn the complete ins and outs of practica

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Become an advanced practitioner with this progressive set of master classes on application-oriented machine learningKey Features <ul> <li>Comprehensive coverage of key topics in machine learning with an emphasis on both the theoretical and practical aspects</li> <li>More than 15 open source Java tools in a wide range of techniques, with code and practical usage.</li> <li>More than 10 real-world case studies in machine learning highlighting techniques ranging from data ingestion up to analyzing the results of experiments, all preparing the user for the practical, real-world use of tools and data analysis.</li> </ul> Book Description Java is one of the main languages used by practicing data scientists; much of the Hadoop ecosystem is Java-based, and it is certainly the language that most production systems in Data Science are written in. If you know Java, Mastering Machine Learning with Java is your next step on the path to becoming an advanced practitioner in Data Science. This book aims to introduce you to an array of advanced techniques in machine learning, including classification, clustering, anomaly detection, stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, deep learning, and big data batch and stream machine learning. Accompanying each chapter are illustrative examples and real-world case studies that show how to apply the newly learned techniques using sound methodologies and the best Java-based tools available today. On completing this book, you will have an understanding of the tools and techniques for building powerful machine learning models to solve data science problems in just about any domain. What you will learn <ul> <li>Master key Java machine learning libraries, and what kind of problem each can solve, with theory and practical guidance.</li> <li>Explore powerful techniques in each major category of machine learning such as classification, clustering, anomaly detection, graph modeling, and text mining.</li> <li>Apply machine learning to real-world data with methodologies, processes, applications, and analysis.</li> <li>Techniques and experiments developed around the latest specializations in machine learning, such as deep learning, stream data mining, and active and semi-supervised learning.</li> <li>Build high-performing, real-time, adaptive predictive models for batch- and stream-based big data learning using the latest tools and methodologies.</li> <li>Get a deeper understanding of technologies leading towards a more powerful AI applicable in various domains such as Security, Financial Crime, Internet of Things, social networking, and so on.</li> </ul> Who this book is for This book will appeal to anyone with a serious interest in topics in Data Science or those already working in related areas: ideally, intermediate-level data analysts and data scientists with experience in Java. Preferably, you will have experience with the fundamentals of machine learning and now have a desire to explore the area further, are up to grappling with the mathematical complexities of its algorithms, and you wish to learn the complete ins and outs of practica