کتاب دانلود علوم مهندسی و فناوریرایانه، اینترنت و فناوری اطلاعات
یادگیری ماشین و داده های بزرگ - مفاهیم، الگوریتم ها، ابزار ها و برنامه های کاربردی
یادگیری ماشین و داده های بزرگ - مفاهیم، الگوریتم ها، ابزار ها و برنامه های کاربردی

Machine Learning and Big Data - Concepts, Algorithms, Tools and Applications
سال انتشار: 2020
تعداد صفحات: 516
زبان فایل: انگلیسی
فرمت فایل: pdf
حجم فایل: 32MB
رمز فایل: www.ketabdownload.com
قیمت: 200,000ريال

افزودن به سبد دانلود

Publisher : Wiley-Scrivener
ISBN-10 : 1119654742
ISBN-13 : 978-1119654742
ASIN : B08HH12WKX

Currently many different application areas for Big Data (BD) and Machine Learning (ML) are being explored. These promising application areas for BD/ML are the social sites, search engines, multimedia sharing sites, various stock exchange sites, online gaming, online survey sites and various news sites, and so on.  To date, various use-cases for this application area are being researched and developed. Software applications are already being published and used in various settings from education and training to discover useful hidden patterns and other information like customer choices and market trends that can help organizations make more informed and customer-oriented business decisions.

Combining BD with ML will provide powerful, largely unexplored application areas that will revolutionize practice in Videos Surveillance, Social Media Services, Email Spam and Malware Filtering, Online Fraud Detection, and so on.  It is very important to continuously monitor and understand these effects from safety and societal point of view.

Hence, the main purpose of this book is for researchers, software developers and practitioners, academicians and students to showcase novel use-cases and applications, present empirical research results from user-centered qualitative and quantitative experiments of these new applications, and facilitate a discussion forum to explore the latest trends in big data and machine learning by providing algorithms which can be trained to perform interdisciplinary techniques such as statistics, linear algebra, and optimization and also create automated systems that can sift through large volumes of data at high speed to make predictions or decisions without human intervention.