This book examines the relationship between social capital and sense of well-being among China’s youth. This is a population on whose well-being hangs the long-term social and economic development prospects of the entire country. Understanding the forces that shape the subjective beliefs and objective experiences of Chinese youth takes on urgency in a time of rapid and profound social change. Social scientists have increasingly been interested in the pivotal role access to social capital p...

This book showcases developments in Chinese for specific and professional purposes. It covers various topics, such as Business Chinese, Chinese for Academic Purposes, Chinese for Tourism, Chinese for Science and Technology, and Chinese for Medical Purposes. As a reflection of the fact that research on Business Chinese dominates the academic field of Chinese for specific purposes, nine (out of sixteen) chapters in this volume address issues related to intercultural communication and approac...

Big data and artificial intelligence (AI) cannot remain limited to academic theoretical research. It is crucial to utilize them in practical business scenarios, enabling cutting-edge technology to generate tangible value. This book delves into the application of AI from theory to practice, offering detailed insights into AI project design and code implementation across eleven business scenarios in four major sectors: retail banking, e-banking, bank credit, and tech operations. It provides ...

Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent...

The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice.In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatab...

Understand, evaluate, and visualize dataAbout This BookLearn basic steps of data analysis and how to use Python and its packagesA step-by-step guide to predictive modeling including tips, tricks, and best practicesEffectively visualize a broad set of analyzed data and generate effective resultsWho This Book Is ForThis book is for Python Developers who are keen to get into data analysis and wish to visuali...

Knowledge of business research is necessary for any business student, as all postgraduate business programmes and business-related disciplines require it. Business Research is a groundbreaking book for student researchers who need to conceive, conduct and complete a new research project for the first time. It concentrates on the business organization and gives invaluable practical advice on going out into the field and conducting interviews, researching problems a...

Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain...

Gain practical insights by exploiting data in your business to build advanced predictive modeling applicationsKey FeaturesA step-by-step guide to predictive modeling including lots of tips, tricks, and best practicesLearn how to use popular predictive modeling algorithms such as Linear Regression, Decision Trees, Logistic Regression, and ClusteringMaster open source Python tools to build sophisticated predictive models...

It sounds like a paradox: How do you engage in autoethnography collaboratively? Heewon Chang, Faith Ngunjiri, and Kathy-Ann Hernandez break new ground on this blossoming new array of research models, collectively labeled Collaborative Autoethnography. Their book serves as a practical guide by providing you with a variety of data collection, analytic, and writing techniques to conduct collaborative projects. It also answers your questions about the bigger picture: What advantages does a col...

Build Machine Learning models with a sound statistical understanding.Key FeaturesLearn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics.Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering.Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python.Book DescriptionCompl...

Case studies are a powerful pedagogical tool for illuminating constructs and models in real-life contexts. Covering a wide range of teaching-learning contexts and offering in-depth analyses of ESL/ELT language curriculum design issues, this casebook is distinctive and unique in that each case draws on and is clearly linked to a single model presented in Nation and Macalister’s Language Curriculum Design (www.routledge.com/9780415806060), giving the book a high degree of coherence....