"Library and Information Science Trends and Research: Europe", co-edited by Professor Amanda Spink and Dr. Jannica Heinstrom provides an understanding of the new directions in library and information science/management, education and research in Europe. The volume focuses on new research directions
within the field but will also discuss curriculum changes due to the rapidly developing information world. Europe has developed substantially both socially and economically in the last ten years with a growing population and economy. The field of library and information science/management has also
grown in educational and research developments as information increasingly has become a part of people's everyday social and life processes. This book is directly relevant to information scientists, information professionals and librarians, social scientists and people interested in understanding
more about the trends and research in library and information science/management in the European region. Undergraduate and graduate students, academics, educators, and information professionals interested in library and information science will find this book of particular benefit.
Stay updated with expert techniques for solving data analytics and machine learning challenges and gain insights from complex projects and power up your applications
Key Features
Build independent machine learning (ML) systems leveraging the best features of R 3.5
Understand and apply different machine learning techniques using real-world examples
Use methods such as multi-class classification, regression, and clustering
Book Description
Given the growing popularity of the R-zerocost statistical programming environment, there has never been a better time to start applying ML to your data. This book will teach you advanced techniques in ML ,using? the latest code in R 3.5. You will delve into various complex features of supervised
learning, unsupervised learning, and reinforcement learning algorithms to design efficient and powerful ML models.
This newly updated edition is packed with fresh examples covering a range of tasks from different domains. Mastering Machine Learning with R starts by showing you how to quickly manipulate data and prepare it for analysis. You will explore simple and complex models and understand how to compare
them. You'll also learn to use the latest library support, such as TensorFlow and Keras-R, for performing advanced computations. Additionally, you'll explore complex topics, such as natural language processing (NLP), time series analysis, and clustering, which will further refine your skills in
developing applications. Each chapter will help you implement advanced ML algorithms using real-world examples. You'll even be introduced to reinforcement learning, along with its various use cases and models. In the concluding chapters, you'll get a glimpse into how some of these blackbox models
can be diagnosed and understood.
By the end of this book, you'll be equipped with the skills to deploy ML techniques in your own projects or at work.
What you will learn
Prepare data for machine learning methods with ease
Understand how to write production-ready code and package it for use
Produce simple and effective data visualizations for improved insights
Master advanced methods, such as Boosted Trees and deep neural networks
Use natural language processing to extract insights in relation to text
Implement tree-based classifiers, including Random Forest and Boosted Tree
Who this book is for
This book is for data science professionals, machine learning engineers, or anyone who is looking for the ideal guide to help them implement advanced machine learning algorithms. The book will help you take your skills to the next level and advance further in this field. Working knowledge of machine
learning with R is mandatory.
Jeremy Harris Lipschultz, Karen Freberg, Regina Luttrell
£200.00
Book + eBook
Joining a thriving field of new media, this collective volume authored by global academics features important research by thought leaders within computer-mediated communication (CMC) and social media. Featuring 40 comprehensive chapters of new research that focuses on what is new, relevant, and
cutting edge in the areas of CMC and social media, authors critically explore topics ranging from social media theories to civil rights. Divided into three parts, the handbook begins with theory and methods, which sets the foundation for the text and then moves into the applicability of strategy,
tactics, and measurement. The final focus is toward the future of CMC and social media and its impact on the study and practice of communication.
Uniquely relating social media communication research to its computer-mediated communication foundation, as well as digital and emerging media trends, this handbook is an indispensable resource whether you're a graduate student or a seasoned practitioner.