Название: Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook, Third EditionАвтор: Lior Rokach, Oded Maimon, Erez Shmueli
Издательство: Springer
Год: 2023
Страниц: 975
Язык: английский
Формат: pdf (true)
Размер: 26.5 MB
Data Science and Machine Learning major concepts, challenges are presented.
Since the last edition, this field has continued to evolve and to gain popularity. Existing methods are constantly being improved and new methods, applications and aspects are introduced. The new title of this handbook and its content reflect these changes thoroughly. Some existing chapters have been brought up to date. In addition to major revision of the existing chapters, the new edition includes totally new topics, such as: Deep Learning, explainable AI, human factors and social issues and advanced methods for Big Data. The significant enhancement to the content reflects the growth in importance of Data Science. The third edition is also a timely opportunity to incorporate many other changes based on peers and students’ feedback.
This comprehensive handbook also presents a coherent and unified repository of Data Science major concepts, theories, methods, trends, challenges and applications. It covers all the crucial important Machine Learning methods used in Data Science.
Today's accessibility and abundance of data make Data Science matters of considerable importance and necessity. Given the field's recent growth, it's not surprising that researchers and practitioners now have a wide range of methods and tools at their disposal. While statistics is fundamental for Data Science, methods originated from Artificial Intelligence, particularly Machine Learning, are also playing a significant role.
Recommender systems are growing more popular mainly due to its usefulness in real-world applications. To become familiar with the concepts and techniques in recommender systems, it is a good idea to get some hands-on experiences. However, implementing a recommender system from scratch is usually troublesome and time-consuming. As such, we collected some open-source recommendation libraries that aim to help us demonstrate or build a simple recommender model easily.
• MyMediaLite. It is an open-source recommendation library published in 2011. It supports three programming languages: C#, Clojure, and F#. It provides algorithms on both rating prediction and item ranking tasks.
• DeepRec. It is an open-source library for recommendation with deep neural networks. It is a Python library that uses TensorFlow as its backend and addresses tasks such as rating prediction, item ranking, and sequence-aware recommendation.
• LibRec. It is a Java library for recommendation. It aims to solve the rating prediction and item ranking tasks. A number of traditional recommendation algorithms are provided.
• Suprise. It is a Python toolkit that provides a limited amount of rating prediction models.
• OpenRec. OpenRec is also a Python recommendation library. In this library, each recommender is a structured ensemble of reusable modules. However, there are only a few algorithms implemented.
This handbook aims to serve as the main reference for researchers in the fields of information technology, e-Commerce, information retrieval, Data Science, Machine Learning, data mining, databases and statistics as well as advanced level students studying computer science or electrical engineering. Practitioners working within these related fields and data scientists will also want to purchase this handbook as a reference.
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