{"product_id":"machine-learning-for-imbalanced-data-tackle-imbalanced-datasets-using-machine-learning-and-deep-learning-techniques-paperback","title":"Machine Learning for Imbalanced Data: Tackle imbalanced datasets using machine learning and deep learning techniques - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eKumar Abhishek\u003c\/b\u003e (Author), \u003cb\u003eMounir Abdelaziz\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eTake your machine learning expertise to the next level with this essential guide, utilizing libraries like imbalanced-learn, PyTorch, scikit-learn, pandas, and NumPy to maximize model performance and tackle imbalanced data\u003c\/strong\u003e\u003c\/p\u003eKey Features\u003cul\u003e\n\u003cli\u003eUnderstand how to use modern machine learning frameworks with detailed explanations, illustrations, and code samples\u003c\/li\u003e\n\u003cli\u003eLearn cutting-edge deep learning techniques to overcome data imbalance\u003c\/li\u003e\n\u003cli\u003eExplore different methods for dealing with skewed data in ML and DL applications\u003c\/li\u003e\n\u003cli\u003ePurchase of the print or Kindle book includes a free eBook in the PDF format\u003c\/li\u003e\n\u003c\/ul\u003eBook Description\u003cp\u003eAs machine learning practitioners, we often encounter imbalanced datasets in which one class has considerably fewer instances than the other. Many machine learning algorithms assume an equilibrium between majority and minority classes, leading to suboptimal performance on imbalanced data. This comprehensive guide helps you address this class imbalance to significantly improve model performance.\u003c\/p\u003e\u003cp\u003eMachine Learning for Imbalanced Data begins by introducing you to the challenges posed by imbalanced datasets and the importance of addressing these issues. It then guides you through techniques that enhance the performance of classical machine learning models when using imbalanced data, including various sampling and cost-sensitive learning methods.\u003c\/p\u003e\u003cp\u003eAs you progress, you'll delve into similar and more advanced techniques for deep learning models, employing PyTorch as the primary framework. Throughout the book, hands-on examples will provide working and reproducible code that'll demonstrate the practical implementation of each technique.\u003c\/p\u003e\u003cp\u003eBy the end of this book, you'll be adept at identifying and addressing class imbalances and confidently applying various techniques, including sampling, cost-sensitive techniques, and threshold adjustment, while using traditional machine learning or deep learning models.\u003c\/p\u003eWhat you will learn\u003cul\u003e\n\u003cli\u003eUse imbalanced data in your machine learning models effectively\u003c\/li\u003e\n\u003cli\u003eExplore the metrics used when classes are imbalanced\u003c\/li\u003e\n\u003cli\u003eUnderstand how and when to apply various sampling methods such as over-sampling and under-sampling\u003c\/li\u003e\n\u003cli\u003eApply data-based, algorithm-based, and hybrid approaches to deal with class imbalance\u003c\/li\u003e\n\u003cli\u003eCombine and choose from various options for data balancing while avoiding common pitfalls\u003c\/li\u003e\n\u003cli\u003eUnderstand the concepts of model calibration and threshold adjustment in the context of dealing with imbalanced datasets\u003c\/li\u003e\n\u003c\/ul\u003eWho this book is for\u003cp\u003eThis book is for machine learning practitioners who want to effectively address the challenges of imbalanced datasets in their projects. Data scientists, machine learning engineers\/scientists, research scientists\/engineers, and data scientists\/engineers will find this book helpful. Though complete beginners are welcome to read this book, some familiarity with core machine learning concepts will help readers maximize the benefits and insights gained from this comprehensive resource.\u003c\/p\u003eTable of Contents\u003col\u003e\n\u003cli\u003eIntroduction to Data Imbalance in Machine Learning\u003c\/li\u003e\n\u003cli\u003eOversampling Methods\u003c\/li\u003e\n\u003cli\u003eUndersampling Methods\u003c\/li\u003e\n\u003cli\u003eEnsemble Methods\u003c\/li\u003e\n\u003cli\u003eCost-Sensitive Learning\u003c\/li\u003e\n\u003cli\u003eData Imbalance in Deep Learning\u003c\/li\u003e\n\u003cli\u003eData-Level Deep Learning Methods\u003c\/li\u003e\n\u003cli\u003eAlgorithm-Level Deep Learning Techniques\u003c\/li\u003e\n\u003cli\u003eHybrid Deep Learning Methods\u003c\/li\u003e\n\u003cli\u003eModel Calibration\u003c\/li\u003e\n\u003cli\u003eAppendix\u003c\/li\u003e\n\u003c\/ol\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 344\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.72 x 9.25 x 7.5 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e November 30, 2023\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":47213203980537,"sku":"9781801070836","price":74.86,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0789\/2782\/3097\/files\/MySH4hHW2-9781801070836.webp?v=1768104303","url":"https:\/\/bookscloud.io\/products\/machine-learning-for-imbalanced-data-tackle-imbalanced-datasets-using-machine-learning-and-deep-learning-techniques-paperback","provider":"BooksCloud Book Dropshipping","version":"1.0","type":"link"}