Machine learning using R

Get comfortable with Google Sketchup 8 fast, with this media-rich, customized, hands-on learning experience! ? Practical, approachable coverage flattens the Sketchup 8 learning curve Tightly in

Kaplowitz, a retired librarian at UCLA who offers workshops in active learning, assessment, expected learning outcomes, instructional planning, and learner-centered teaching, explores the fundamentals

The most complete, powerful iPad learning product! Text, video and audio in one tightly-integrated, hands-on learning experience. Covers both the iPad 2 and the original iPad Also includes co

The field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book examines various aspects of the evaluation process with an emphasis on classification algorithms. The authors describe several techniques for classifier performance assessment, error estimation and resampling, obtaining statistical significance as well as selecting appropriate domains for evaluation. They also present a unified evaluation framework and highlight how different components of evaluation are both significantly interrelated and interdependent. The techniques presented in the book are illustrated using R and WEKA, facilitating better practical insight as well as implementation. Aimed at researchers in the theory and applications of machine learning, this book offers a solid basis for c

It is widely recognised that we are living through an 'age of the narrative'. Many of the constituent disciplines in the social sciences resonate with this trend by using life history and narrative ap

Thoroughly updated to AutoCAD® 2011, this easy-to-understand essentials edition covers all aspects of AutoCAD® fundamentals, from an introduction to computer hardware and AutoCAD&r

Researchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methodology since the late seventies. Thus, they are faced with a w

Developed specifically for graduate students in health education and health sciences, this book offers coverage of research methods integrated with guidance on writing the five-chapter thesis or disse

Written specifically for non-language arts teachers, this resource focuses on using writing as an instructional tool to deepen and expand student understanding in the content areas.

Written specifically for non-language arts teachers, this resource focuses on using writing as an instructional tool to deepen and expand student understanding in the content areas.

This book is designed for upper elementary level teachers who wish to integrate science, literacy, and children's literature in creative ways. Third through fifth graders will discover, using hands-o

The second edition provides detailed sample lesson plans and includes additional strategies for using extended time formats effectively.

This book is a companion book to the comprehensive text entitled Image Processing, Analysis, and Machine Vision by M. Sonka, V. Hlavac, and R. Boyle. This workbook provides additional material for rea

There is an intricate literacy to Gardner's multiple intelligences theory that unlocks key entry points for differentiated learning. Using a well-articulated framework, rich with graphic representati

Using discrete dynamical systems, this book introduces powerful mathematical modeling techniques, both standard analytical and modern computational, to students in mathematics, the natural sciences, a

This workbook is for teachers and administrators who must make sense of data collected in schools, such as state student achievement test results and other qualitative and quantitative data. It uses r

Based on five years of research in high school and community college programs, this book explores the potential for using work-based learning as part of a broad education reform strategy.

Using simple plans and photographs this booklet describes the construction and assembly of this machine, whose main feature is the ability to form wheel rims from cold flat mild steel; this wheel can

The book elucidates the revolutionary and riveting research in the ultramodern domain of drone technologies, drone-enabled IoT applications, and artificial intelligence-based smart surveillance. The book explains the most recent developments in the field, challenges, and future scope of drone technologies. Beyond that, it discusses the importance of a wide range of design applications, drone/UAV development, and drone-enabled smart healthcare systems for smart cities. It describes pioneering work on mitigating cyber security threats by employing intelligent machine learning models in the designing of IoT-aided drones. The book also has a fascinating chapter on application intrusion detection by drones using recurrent neural networks. Other chapters address interdisciplinary fields like artificial intelligence, deep learning, the role of drones in healthcare in smart cities, and the importance of drone technology in agriculture.

This new volume addresses the growing interest in and use of big data analytics in many industries and in many research fields around the globe; it is a comprehensive resource on the core concepts of big data analytics and the tools, techniques, and methodologies. The book gives the why and the how of big data analytics in an organized and straightforward manner, using both theoretical and practical approaches.The book's authors have organized the contents in a systematic manner, starting with an introduction and overview of big data analytics and then delving into pre-processing methods, feature selection methods and algorithms, big data streams, and big data classification. Such terms and methods as swarm intelligence, data mining, the bat algorithm and genetic algorithms, big data streams, and many more are discussed. The authors explain how deep learning and machine learning along with other methods and tools are applied in big data analytics. The last section of the book presents

Cybersecurity attacks grow more complex every day. The first edition of Wilson's COMPTIA PENTEST+ GUIDE TO PENETRATION TESTING helps you gain the knowledge and skills necessary to analyze systems in the same way hackers do. It provides the information you need to successfully pass the CompTIA PenTest+ PTO-002 certification exam and contains detailed examples, projects, and hands-on activities (including virtual machine labs and interactive online features) where you can practice using the actual tools and methodologies needed to be a successful penetration tester. The author begins by exploring the concept of penetration testing, and then walks you through the process of building your own penetration testing lab of virtual machines (hosted on your own computer, which is used to practice the use of pen testing tools and methods). A final project guides you in the creation of a penetration testing report that includes all the areas covered in earlier modules.

Develop the analytical skills that are in high demand in businesses today with Camm/Cochran/Fry/Ohlmann's best-selling BUSINESS ANALYTICS, 5E. You master the full range of analytics as you strengthen descriptive, predictive and prescriptive analytic skills. Real examples and memorable visuals clearly illustrate data and results. Step-by-step instructions guide you through using Excel, Tableau, R or the Python-based Orange data mining software to perform advanced analytics. Practical, relevant problems at all levels of difficulty let you apply what you've learned. Updates throughout this edition address topics beyond traditional quantitative concepts, such as data wrangling, data visualization and data mining, which are increasingly important in today's business environment. MindTap and WebAssign online learning platforms are also available with an interactive eBook, algorithmic practice problems and Exploring Analytics visualizations to strengthen your understanding of key concepts.

An empirical study and analysis of heart disease prediction involves using data analysis techniques to identify patterns and risk factors associated with cardiovascular disease. This approach utilizes machine learning algorithms to classify patients based on their likelihood of developing heart disease.The study involves collecting data on risk factors such as age, gender, family history, blood pressure, cholesterol levels, smoking, and diabetes. Feature selection techniques are used to identify the most important risk factors, and a classification model is trained using these factors. The accuracy of the model is evaluated using metrics such as sensitivity, specificity, and AUC.This empirical study and analysis has several advantages, including the ability to identify new risk factors associated with heart disease, improved accuracy in predicting cardiovascular risk, and the potential to develop more personalized prevention and treatment strategies. This approach has the potential to

The Teenage years are critical for learning, understanding the world, and discovering new hobbies and passions. It is a period of maturation and self-discovery during which adolescents learn more about themselves and their place in the world.These are exciting times, but they may also be intimidating. It's easy to feel overwhelmed by the responsibilities of growing up, with so much to learn and so many new experiences.This is where this book comes into play. To assist kids in navigating one of the most important obligations and obstacles that every young adult must face: money.Ferne Bowe (author of Life Skills for Young Adults and Life Skills for Tweens)'s Skills for Teenagers teaches a teen what he or she needs to know about personal money. This book covers all of the fundamental money skills that every teenager should know, from budgeting and saving to investing and credit development.The book provides practical guidance to help kids build a healthy relationship with money by using r

內容介紹: 別再傻傻只選一個模型訓練很多模型，卻不知道應該選哪一個？沒有一個模型達標？每個模型都有其優缺點，無法取捨？小朋友才做選擇，大人全都要！你該試試集成式學習！集成式學習是使用 2 種或更多的機器學習演算法，來組合出預測能力更好的模型。DeepMind 已經使用集成式學習來組合多個神經網路，控制 Google 資料中心的運作效能；集成式學習技術也在 Kaggle 平台上，席捲了各個競賽的第一名寶座。因此，集成式學習是建立出更具威力的模型，不可或缺的技術之一。本書會介紹實務上常見的集成式學習演算法，如硬投票、軟投票、堆疊法、自助聚合法、適應提升法、梯度提升法、隨機森林、極端隨機樹等，並且使用熱門的 scikit-learn、Keras、OpenEnsembles、XGBoost 等 Python 函式庫來實作各種不同的集成式學習技術，建構出一個強大的模型。熟稔本書的內容後，不但可以精通集成式學習，在實際情境中面對問題時，亦能具備充分的專業知識判斷適用的集成式學習方法，並成功實作它們。書中採用「做中學」的方式，讓你不僅可以快速掌握理論基礎，也能了解各種集成式學習技術的實作，再加上運用真實世界中的資料集，你將能夠建立出更佳的機器學習模型，以解決各種問題，包含迴歸、分類、分群。現在翻開本書，讓我們一起進入集成式學習的世界，整合你所會的全部技術，打造最強大的模型。 本書特色: ● 繁體中文第 1 本集成式學習專書，告訴你不要再傻傻地只選一個模型● 完整介紹集成式學習中常見的演算法，包含極端隨機樹、堆疊法、自助聚合法、提升法等● 一書掌握實作集成式學習的必備套件，如 Scikit-Learn、OpenEnsembles、XGBoost 等● 用 Python 刻演算法給你看，接著告訴你怎麼用套件。讓你不只會做，還懂為什麼這麼做● 以 5 個實務案例來展示集成式學習的威力● 本書由施威銘研究室監修，內容易讀易懂，並加入大量「小編補充」補充必要知識● 本書 Python 範例程式免費下載

Chemical Oceanography: Element Fluxes in the Sea focuses on the use of chemical distributions to understand mechanisms of physical, chemical, biological, and geological processes in the ocean. After an introduction describing observed chemical concentrations, chapters focus on using chemical tracers to determine fluxes on a variety of time scales. Long-term chemical cycles are dominated by exchanges between seawater and land, sediments, and underwater volcanoes. Biological and ocean mixing processes dominate internal chemical cycles that respond to changes on hundred- to thousand-year time scales. Stable and radioactive isotopes trace the fluxes of nutrients and carbon to quantify the rates and mechanisms of chemical cycles. Anthropogenic influences - which have grown to be of the same magnitude as some natural cycles - are a specific focus throughout the book. Discussion boxes and quantitative problems help instructors to deepen student learning. Appendices enhance the book's utility

Marketing and sales prioritize AI and machine learning more than any other business department, yet often struggle with how to scale and strategize the opportunities it presents.AI Strategy for Sales and Marketing presents a framework for understanding how AI can boost customer-centricity and sales by creating a connected strategy that delivers value today and into the future. Supported by practical tips and advice throughout, it covers topics including personalization, upskilling, customer experience for both on and offline shopping channels and the importance of using AI responsibly to create consumer trust. Featuring original research and interviews with leading practitioners, it also contains global case studies from organizations in a range of sectors, including Samsung, LinkedIn, PwC, Rolls Royce and Hilton, with insights into the various stages of their adoption journeys. Written by a recognized industry expert, it is an invaluable resource for those wanting to benefit from usin

Marketing and sales prioritize AI and machine learning more than any other business department, yet often struggle with how to scale and strategize the opportunities it presents.AI Strategy for Sales and Marketing presents a framework for understanding how AI can boost customer-centricity and sales by creating a connected strategy that delivers value today and into the future. Supported by practical tips and advice throughout, it covers topics including personalization, upskilling, customer experience for both on and offline shopping channels and the importance of using AI responsibly to create consumer trust. Featuring original research and interviews with leading practitioners, it also contains global case studies from organizations in a range of sectors, including Samsung, LinkedIn, PwC, Rolls Royce and Hilton, with insights into the various stages of their adoption journeys. Written by a recognized industry expert, it is an invaluable resource for those wanting to benefit from usin

This Element provides a basic introduction to sentiment analysis, aimed at helping students and professionals in corpus linguistics to understand what sentiment analysis is, how it is conducted, and where it can be applied. It begins with a definition of sentiment analysis and a discussion of the domains where sentiment analysis is conducted and used the most. Then, it introduces two main methods that are commonly used in sentiment analysis known as supervised machine-learning and unsupervised learning (or lexicon-based) methods, followed by a step-by-step explanation of how to perform sentiment analysis with R. The Element then provides two detailed examples or cases of sentiment and emotion analysis, with one using an unsupervised method and the other using a supervised learning method.

A comprehensive introduction and teaching resource for state-of-the-art Qualitative Comparative Analysis (QCA) using R software. This guide facilitates the efficient teaching, independent learning, and use of QCA with the best available software, reducing the time and effort required when encountering not just the logic of a new method, but also new software. With its applied and practical focus, the book offers a genuinely simple and intuitive resource for implementing the most complete protocol of QCA. To make the lives of students, teachers, researchers, and practitioners as easy as possible, the book includes learning goals, core points, empirical examples, and tips for good practices. The freely available online material provides a rich body of additional resources to aid users in their learning process. Beyond performing core analyses with the R package QCA, the book also facilitates a close integration with the R package SetMethods allowing for a host of additional protocols for

A comprehensive introduction and teaching resource for state-of-the-art Qualitative Comparative Analysis (QCA) using R software. This guide facilitates the efficient teaching, independent learning, and use of QCA with the best available software, reducing the time and effort required when encountering not just the logic of a new method, but also new software. With its applied and practical focus, the book offers a genuinely simple and intuitive resource for implementing the most complete protocol of QCA. To make the lives of students, teachers, researchers, and practitioners as easy as possible, the book includes learning goals, core points, empirical examples, and tips for good practices. The freely available online material provides a rich body of additional resources to aid users in their learning process. Beyond performing core analyses with the R package QCA, the book also facilitates a close integration with the R package SetMethods allowing for a host of additional protocols for

Master the lucrative discipline of quantitative trading with this insightful handbook from a master in the field In the newly revised Second Edition of Quantitative Trading: How to Build Your Own Algorithmic Trading Business, quant trading expert Dr. Ernest P. Chan shows you how to apply both time-tested and novel quantitative trading strategies to develop or improve your own trading firm.You'll discover new case studies and updated information on the application of cutting-edge machine learning investment techniques, as well as: Updated back tests on a variety of trading strategies, with included Python and R code examplesA new technique on optimizing parameters with changing market regimes using machine learning. A guide to selecting the best traders and advisors to manage your money Perfect for independent retail traders seeking to start their own quantitative trading business, or investors looking to invest in such traders, this new edition of Quantitative Trading will also earn a

Implement real-time data processing applications on the Raspberry Pi. This book uniquely helps you work with data science concepts as part of real-time applications using the Raspberry Pi as a localized cloud. You'll start with a brief introduction to data science followed by a dedicated look at the fundamental concepts of Python programming. Here you'll install the software needed for Python programming on the Pi, and then review the various data types and modules available. The next steps are to set up your Pis for gathering real-time data and incorporate the basic operations of data science related to real-time applications. You'll then combine all these new skills to work with machine learning concepts that will enable your Raspberry Pi to learn from the data it gathers. Case studies round out the book to give you an idea of the range of domains where these concepts can be applied. By the end of Data Science with the Raspberry Pi, you'll understand that many applications are now de

This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry-relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by 360 practice questions and 120 data exercises. Extensive online resources, including raw and cleaned data and codes for all analysis in Stata, R, and Python, can be found at www.gabors-data-analysis.com.

This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry-relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by 360 practice questions and 120 data exercises. Extensive online resources, including raw and cleaned data and codes for all analysis in Stata, R, and Python, can be found at www.gabors-data-analysis.com.

This Element discusses how shiny, an R package, can help instructors teach quantitative methods more effectively by way of interactive web apps. The interactivity increases instructors' effectiveness by making students more active participants in the learning process, allowing them to engage with otherwise complex material in an accessible, dynamic way. The Element offers four detailed apps that cover two fundamental linear regression topics: estimation methods (least squares, maximum likelihood) and the classic linear regression assumptions. It includes a summary of what the apps can be used to demonstrate, detailed descriptions of the apps' full capabilities, vignettes from actual class use, and example activities. Two other apps pertain to a more advanced topic (LASSO), with similar supporting material. For instructors interested in modifying the apps, the Element also documents the main apps' general code structure, highlights some of the more likely modifications, and goes through

It will soon be impossible to tell what is real and what is fake. Recent advances in AI mean that by scanning images of a person (for example using Facebook), a powerful machine learning system can