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Applied Linear Algebra and Matrix Methods (Springer Undergraduate Texts in Mathematics and Technology)
by Timothy G. FeemanThis textbook is designed for a first course in linear algebra for undergraduate students from a wide range of quantitative and data driven fields. By focusing on applications and implementation, students will be prepared to go on to apply the power of linear algebra in their own discipline. With an ever-increasing need to understand and solve real problems, this text aims to provide a growing and diverse group of students with an applied linear algebra toolkit they can use to successfully grapple with the complex world and the challenging problems that lie ahead. Applications such as least squares problems, information retrieval, linear regression, Markov processes, finding connections in networks, and more, are introduced on a small scale as early as possible and then explored in more generality as projects. Additionally, the book draws on the geometry of vectors and matrices as the basis for the mathematics, with the concept of orthogonality taking center stage. Important matrix factorizations as well as the concepts of eigenvalues and eigenvectors emerge organically from the interplay between matrix computations and geometry.The R files are extra and freely available. They include basic code and templates for many of the in-text examples, most of the projects, and solutions to selected exercises. As much as possible, data sets and matrix entries are included in the files, thus reducing the amount of manual data entry required.
Applied Linear Algebra, Probability and Statistics: A Volume in Honour of C. R. Rao and Arbind K. Lal (Indian Statistical Institute Series)
by Ravindra B. Bapat Manjunatha Prasad Karantha Stephen J. Kirkland Samir Kumar Neogy Sukanta Pati Simo PuntanenThis book focuses on research in linear algebra, statistics, matrices, graphs and their applications. Many chapters in the book feature new findings due to applications of matrix and graph methods. The book also discusses rediscoveries of the subject by using new methods. Dedicated to Prof. Calyampudi Radhakrishna Rao (C.R. Rao) who has completed 100 years of legendary life and continues to inspire us all and Prof. Arbind K. Lal who has sadly departed us too early, it has contributions from collaborators, students, colleagues and admirers of Professors Rao and Lal. With many chapters on generalized inverses, matrix analysis, matrices and graphs, applied probability and statistics, and the history of ancient mathematics, this book offers a diverse array of mathematical results, techniques and applications. The book promises to be especially rewarding for readers with an interest in the focus areas of applied linear algebra, probability and statistics.
Applied Linear Models with SAS
by Daniel ZeltermanThis textbook for a second course in basic statistics for undergraduates or first-year graduate students introduces linear regression models and describes other linear models including Poisson regression, logistic regression, proportional hazards regression, and nonparametric regression. Numerous examples drawn from the news and current events with an emphasis on health issues illustrate these concepts. Assuming only a pre-calculus background, the author keeps equations to a minimum and demonstrates all computations using SAS. Most of the programs and output are displayed in a self-contained way, with an emphasis on the interpretation of the output in terms of how it relates to the motivating example. Plenty of exercises conclude every chapter. All of the datasets and SAS programs are available from the book's website, along with other ancillary material.
Applied Linear Regression
by Sanford WeisbergPraise for the Third Edition"...this is an excellent book which could easily be used as a course text..."--International Statistical InstituteThe Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples.Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illustrates how to develop estimation, confidence, and testing procedures primarily through the use of least squares regression. While maintaining the accessible appeal of each previous edition,Applied Linear Regression, Fourth Edition features:Graphical methods stressed in the initial exploratory phase, analysis phase, and summarization phase of an analysisIn-depth coverage of parameter estimates in both simple and complex models, transformations, and regression diagnosticsNewly added material on topics including testing, ANOVA, and variance assumptionsUpdated methodology, such as bootstrapping, cross-validation binomial and Poisson regression, and modern model selection methodsApplied Linear Regression, Fourth Edition is an excellent textbook for upper-undergraduate and graduate-level students, as well as an appropriate reference guide for practitioners and applied statisticians in engineering, business administration, economics, and the social sciences.
Applied Linear Regression for Business Analytics with R: A Practical Guide to Data Science with Case Studies (International Series in Operations Research & Management Science #337)
by Daniel P. McGibneyApplied Linear Regression for Business Analytics with R introduces regression analysis to business students using the R programming language with a focus on illustrating and solving real-time, topical problems. Specifically, this book presents modern and relevant case studies from the business world, along with clear and concise explanations of the theory, intuition, hands-on examples, and the coding required to employ regression modeling. Each chapter includes the mathematical formulation and details of regression analysis and provides in-depth practical analysis using the R programming language.
Applied Linear Regression for Longitudinal Data: With an Emphasis on Missing Observations (Chapman & Hall/CRC Texts in Statistical Science)
by Frans E.S. Tan Shahab JolaniThis book introduces best practices in longitudinal data analysis at intermediate level, with a minimum number of formulas without sacrificing depths. It meets the need to understand statistical concepts of longitudinal data analysis by visualizing important techniques instead of using abstract mathematical formulas. Different solutions such as multiple imputation are explained conceptually and consequences of missing observations are clarified using visualization techniques. Key features include the following: • Provides datasets and examples online • Gives state-of-the-art methods of dealing with missing observations in a non-technical way with a special focus on sensitivity analysis • Conceptualises the analysis of comparative (experimental and observational) studies It is the ideal companion for researcher and students in epidemiological, health, and social and behavioral sciences working with longitudinal studies without a mathematical background. Frans E.S. Tan is an associate professor (retired) of methodology and statisticsat Maastricht University, The Netherlands. Shahab Jolani is an assistant professor of methodology and statistics atMaastricht University, The Netherlands.
Applied Linguistics: Language For The Real World (Introducing Linguistics #Volume 1)
by Li WeiIn this complete survey of the theories, methods, and key findings within applied linguistics, students are introduced to core research questions and the various approaches to tackling these. Provides a comprehensive introduction to this interdisciplinary field of research and practice, dealing with practical issues of language and communication Takes a problem-solving approach, introducing students to key research questions and guiding them through the various ways of tackling these Features additional study aids throughout, including chapter outlines, learning objectives, key terms, research questions and answers, study questions, and recommended further readings Enables students to identify every-day language and communication issues, and to draw on their own personal experiences Edited by a leading figure in the field, heading up an experienced and interdisciplinary team of contributors from the renowned department of applied linguistics at Birkbeck College, University of London – resulting in unique combination of knowledge, skills, and strength from scholars who teach and research together
Applied Linguistics and Language Teaching in the Neo-Nationalist Era
by Kyle McIntoshThis book explores how resurgent nationalism across the globe demands re-examination of many of the theories and practices in applied linguistics and language teaching as political forces seek to limit the movement of people, goods, and services across national borders and, in some cases, enact violence upon those with linguistic and/or ethnic backgrounds that differ from that of the dominant culture. The authors who have contributed to this volume provide careful analysis of nationalist discourses and actions in Brazil, Cameroon, Canada, China, Colombia, Germany, Poland, the United Arab Emirates, the United States, and Vietnam. They offer their unique historical and cultural perspectives on the complex relationship between language, identity, and nationhood in each of these countries, as well as practical responses to the fraught political situations that many language educators and policy makers now face.This book will appeal to researchers in applied linguistics and language teaching, as well as second and foreign language teaching professionals working and living in countries where nationalist sentiments are on the rise.
Applied Linguistics and Primary School Teaching
by Sue Ellis Elspeth MccartneyModern primary teachers must adapt literacy programmes and ensure efficient learning for all. They must also support children with language and literacy difficulties, children learning English as an additional language and possibly teach a modern foreign language. To do this effectively, they need to understand the applied linguistics research that underpins so many different areas of the language and literacy curriculum. This book illustrates the impact of applied linguistics on curriculum frameworks and pedagogy. It captures the range of applied linguistics knowledge that teachers need, and illustrates how this is framed and is used by policy makers, researchers, teacher educators and the other professions who work with teachers in schools. It considers how to effect professional development that works. It is essential reading for primary teachers but also for speech and language therapists, educational psychologists, learning support teachers and all those doing language or literacy research in the primary classroom.
Applied Linguistics in Language Education (A\hodder Arnold Publication)
by Steven McDonough'Applied Linguistics for Language Education' covers those areas of applied language study that are most directly relevant to language teaching, testing, and teacher education. It focuses on the fundamental questions raised for research by the practice of language teaching and research. The reader is thus introduced to the current research climate through consideration of germane controversial issues. If any conclusion about applied linguistic research in the last twenty years is possible, it is that we cannot take anything for granted!Steven McDonough opens with examples of language teaching, teaching materials, and learning a foreign language, which teachers and language learners will recognise, drawing out questions from these which are addressed throughout the rest of the text. Arguments and data from research of all kinds are brought to bear on these and other background issues that are raised, for example: the nature and effects of classroom discourse; the challenges and utility of linguistic theory and linguistic descriptions; what knowing a second language means for proficiency and for processing; nature and nurture in second language learning; how people process language in classrooms and beyond; the role of instruction and the roles of teachers; and measuring achievement.Complex issues are laid out in a clear and accessible style, and many examples are used, mainly, but not exclusively, from English and learning English as a second language. However, the principles apply to learning or teaching any language as a second or foreign language, and 'Applied Linguistics for Language Education' is the most concise overview of current linguistics presently available.
Applied Linguistics in the Indonesian Context: Society, Culture and Language (Engaging Indonesia)
by Richmond Stroupe Lilie RoosmanThis open access edited volume presents a series of studies utilizing a variety of linguistic research techniques to investigate areas of language education, acquisition and assessment, contemporary political debate, modern and historical print media, and clinical language disorders, in the Indonesian context. In doing so, the authors provide a rich and diverse overview of current research in the fields of linguistics and applied linguistics. The initial section focuses on research conducted in educational settings, focusing on English-medium instruction (EMI), reading assessment, discovery-based learning, cultural elements in textbooks, and pre-service teacher preparation in Indonesia, offering recommendations for improving language education. The second section demonstrates the applications of corpus linguistics, focusing on collocation patterns in different languages, lexical use and context of rhetorical markers, and authorship determination. A third section presents investigations related to aspects of historical and contemporary language use in a variety of contexts, including advertisements, political debate, Indonesian print media, and translanguaging and multilingual writings, discussing the social and cultural dimensions of language use in Indonesia. The final section focuses on clinical linguistics, investigating the relationship between language disorders and language use, including spoken narratives provided by patients with Alzheimer's and the structure and time reference use of agrammatic speakers, with valuable insights into the understanding, diagnosis and treatment of language disorders. Brought together in a single volume, the chapters illustrate how linguistic analysis can be applied across a variety of disciplines and research sites. The volume is relevant to linguists, educators, sociologists, historians, and members of the medical community with particular interest in the Indonesian context.
Applied Linguistics in the Real World
by Patricia FriedrichApplied Linguistics in the Real World introduces readers to situations in which applied linguistics can be and is used. Presenting a panoramic view of the interdisciplinary area of applied linguistics and highlighting the diverse range of twenty-first century occupations that have linguistics at their center, this book: Describes, discusses, and furthers the idea that linguistic knowledge is useful everywhere—from forensic investigations to diplomatic talks; from disability studies to creative writing; and from translation studies to machine learning; Breaks new ground, expanding beyond well-established areas of applied-linguistic interest in its inclusion of disability studies, peace studies and the new literature; Provides readers with original research questions and practical applications for them to expand their own research portfolios. Written in an accessible, direct style, Applied Linguistics in the Real World will be essential reading for all students of applied linguistics and is an important addition to the library of anyone who feels passionate and inspired by language matters.
Applied Logic for Computer Scientists: Computational Deduction and Formal Proofs (Undergraduate Topics in Computer Science)
by Mauricio Ayala-Rincón Flávio L. C. de MouraThis book provides an introduction to logic and mathematical induction which are the basis of any deductive computational framework. A strong mathematical foundation of the logical engines available in modern proof assistants, such as the PVS verification system, is essential for computer scientists, mathematicians and engineers to increment their capabilities to provide formal proofs of theorems and to certify the robustness of software and hardware systems. <P><P> The authors present a concise overview of the necessary computational and mathematical aspects of ‘logic’, placing emphasis on both natural deduction and sequent calculus. Differences between constructive and classical logic are highlighted through several examples and exercises. Without neglecting classical aspects of computational logic, the authors also highlight the connections between logical deduction rules and proof commands in proof assistants, presenting simple examples of formalizations of the correctness of algebraic functions and algorithms in PVS. <P> Applied Logic for Computer Scientists will not only benefit students of computer science and mathematics but also software, hardware, automation, electrical and mechatronic engineers who are interested in the application of formal methods and the related computational tools to provide mathematical certificates of the quality and accuracy of their products and technologies.
Applied Logistic Regression (Wiley Series in Probability and Statistics #398)
by Stanley Lemeshow David W. Hosmer Jr. Rodney X. SturdivantA new edition of the definitive guide to logistic regression modeling for health science and other applicationsThis thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables.Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-the-art techniques for building, interpreting, and assessing the performance of LR models. New and updated features include:A chapter on the analysis of correlated outcome dataA wealth of additional material for topics ranging from Bayesian methods to assessing model fitRich data sets from real-world studies that demonstrate each method under discussionDetailed examples and interpretation of the presented results as well as exercises throughoutApplied Logistic Regression, Third Edition is a must-have guide for professionals and researchers who need to model nominal or ordinal scaled outcome variables in public health, medicine, and the social sciences as well as a wide range of other fields and disciplines.
Applied Logistic Regression Analysis (Quantitative Applications in the Social Sciences #106)
by Dr Scott MenardThe focus in this Second Edition is again on logistic regression models for individual level data, but aggregate or grouped data are also considered. The book includes detailed discussions of goodness of fit, indices of predictive efficiency, and standardized logistic regression coefficients, and examples using SAS and SPSS are included. More detailed consideration of grouped as opposed to case-wise data throughout the book Updated discussion of the properties and appropriate use of goodness of fit measures, R-square analogues, and indices of predictive efficiency Discussion of the misuse of odds ratios to represent risk ratios, and of over-dispersion and under-dispersion for grouped data Updated coverage of unordered and ordered polytomous logistic regression models.
Applied Longitudinal Analysis
by Garrett M. Fitzmaurice James H. Ware Nan M. LairdFeatures newly developed topics and applications of the analysis of longitudinal data. Applied Longitudinal Analysis, Second Edition presents modern methods for analyzing data from longitudinal studies and now features the latest state-of-the-art techniques. The book emphasizes practical, rather than theoretical, aspects of methods for the analysis of diverse types of longitudinal data that can be applied across various fields of study, from the health and medical sciences to the social and behavioral sciences.The authors incorporate their extensive academic and research experience along with various updates that have been made in response to reader feedback. The Second Edition features six newly added chapters that explore topics currently evolving in the field, including:Fixed effects and mixed effects models. Marginal models and generalized estimating equations. Approximate methods for generalized linear mixed effects models. Multiple imputation and inverse probability weighted methods. Smoothing methods for longitudinal data. Sample size and power. Each chapter presents methods in the setting of applications to data sets drawn from the health sciences. New problem sets have been added to many chapters, and a related website features sample programs and computer output using SAS, Stata, and R, as well as data sets and supplemental slides to facilitate a complete understanding of the material.With its strong emphasis on multidisciplinary applications and the interpretation of results, Applied Longitudinal Analysis, Second Edition is an excellent book for courses on statistics in the health and medical sciences at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for researchers and professionals in the medical, public health, and pharmaceutical fields as well as those in social and behavioral sciences who would like to learn more about analyzing longitudinal data.
Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence
by Judith D. Singer John B. WillettChange is constant in everyday life. Infants crawl and then walk, children learn to read and write, teenagers mature in myriad ways, the elderly become frail and forgetful. Beyond these natural processes and events, external forces and interventions instigate and disrupt change: test scores may rise after a coaching course, drug abusers may remain abstinent after residential treatment. By charting changes over time and investigating whether and when events occur, researchers reveal the temporal rhythms of our lives. Applied Longitudinal Data Analysis is a much-needed professional book for empirical researchers and graduate students in the behavioral, social, and biomedical sciences. It offers the first accessible in-depth presentation of two of today's most popular statistical methods: multilevel models for individual change and hazard/survival models for event occurrence (in both discrete- and continuous-time). Using clear, concise prose and real data sets from published studies, the authors take you step by step through complete analyses, from simple exploratory displays that reveal underlying patterns through sophisticated specifications of complex statistical models. Applied Longitudinal Data Analysis offers readers a private consultation session with internationally recognized experts and represents a unique contribution to the literature on quantitative empirical methods.
Applied Longitudinal Data Analysis for Epidemiology
by Jos W. R. TwiskThis book discusses the most important techniques available for longitudinal data analysis, from simple techniques such as the paired t-test and summary statistics, to more sophisticated ones such as generalized estimating of equations and mixed model analysis. A distinction is made between longitudinal analysis with continuous, dichotomous and categorical outcome variables. The emphasis of the discussion lies in the interpretation and comparison of the results of the different techniques. The second edition includes new chapters on the role of the time variable and presents new features of longitudinal data analysis. Explanations have been clarified where necessary and several chapters have been completely rewritten. The analysis of data from experimental studies and the problem of missing data in longitudinal studies are discussed. Finally, an extensive overview and comparison of different software packages is provided. This practical guide is essential for non- statisticians and researchers working with longitudinal data from epidemiological and clinical studies.
Applied Longitudinal Data Analysis for Medical Science: A Practical Guide
by Jos W. TwiskEssential for non-statisticians and researchers working with longitudinal data from medical studies, this updated new edition discusses the most important techniques available for analysing data of this type. Using non-technical language, the book explores simple methods such as the paired t-test and summary statistics as well as more sophisticated regression-based methods, including mixed model analysis. The emphasis of the discussion lies in the interpretation of the results of these different methods, covering data analysis with continuous, dichotomous, categorical and other outcome variables. Datasets used throughout the book are provided, enabling readers to re-analyse the examples as they make their way through chapters and improve their understanding of the material. Finally, an extensive and practical overview of, and comparison between, different software packages is provided. Readers will be able to use this book as a practical manual in their everyday work without needing a strong background in statistics.
Applied Machine Learning
by David ForsythMachine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but aren’t necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learning toolkit. Applied Machine Learning covers many topics for people who want to use machine learning processes to get things done, with a strong emphasis on using existing tools and packages, rather than writing one’s own code.A companion to the author's Probability and Statistics for Computer Science, this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use).Emphasizing the usefulness of standard machinery from applied statistics, this textbook gives an overview of the major applied areas in learning, including coverage of:• classification using standard machinery (naive bayes; nearest neighbor; SVM)• clustering and vector quantization (largely as in PSCS)• PCA (largely as in PSCS)• variants of PCA (NIPALS; latent semantic analysis; canonical correlation analysis)• linear regression (largely as in PSCS)• generalized linear models including logistic regression• model selection with Lasso, elasticnet• robustness and m-estimators• Markov chains and HMM’s (largely as in PSCS)• EM in fairly gory detail; long experience teaching this suggests one detailed example is required, which students hate; but once they’ve been through that, the next one is easy• simple graphical models (in the variational inference section)• classification with neural networks, with a particular emphasis onimage classification• autoencoding with neural networks• structure learning
Applied Machine Learning and AI for Engineers: Solve Business Problems That Can't Be Solved Algorithmically
by Jeff ProsiseWhile many introductory guides to AI are calculus books in disguise, this one mostly eschews the math. Instead, author Jeff Prosise helps engineers and software developers build an intuitive understanding of AI to solve business problems. Need to create a system to detect the sounds of illegal logging in the rainforest, analyze text for sentiment, or predict early failures in rotating machinery? This practical book teaches you the skills necessary to put AI and machine learning to work at your company. Applied Machine Learning and AI for Engineers provides examples and illustrations from the AI and ML course Prosise teaches at companies and research institutions worldwide. There's no fluff and no scary equations—just a fast start for engineers and software developers, complete with hands-on examples. This book helps you: Learn what machine learning and deep learning are and what they can accomplishUnderstand how popular learning algorithms work and when to apply themBuild machine learning models in Python with Scikit-Learn, and neural networks with Keras and TensorFlowTrain and score regression models and binary and multiclass classification modelsBuild facial recognition models and object detection modelsBuild language models that respond to natural-language queries and translate text to other languagesUse Cognitive Services to infuse AI into the apps that you write
Applied Machine Learning and Data Analytics: 5th International Conference, AMLDA 2022, Reynosa, Tamaulipas, Mexico, December 22–23, 2022, Revised Selected Papers (Communications in Computer and Information Science #1818)
by M. A. Jabbar Fernando Ortiz-Rodríguez Sanju Tiwari Patrick SiarryThis book constitutes the refereed proceedings of the 5th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2022, held in Reynosa, Tamaulipas, Mexico, during December 22–23, 2022. The 16 full papers and 4 short papers included in this book were carefully reviewed and selected from 89 submissions. They were organized in topical sections as follows: Machine learning, Healthcare and medical imaging informatics; biometrics; forensics; precision agriculture; risk management; robotics and satellite imaging.
Applied Machine Learning and Data Analytics: 6th International Conference, AMLDA 2023, Lübeck, Germany, November 9–10, 2023, Revised Selected Papers (Communications in Computer and Information Science #2047)
by M. A. Jabbar Sanju Tiwari Fernando Ortiz-Rodríguez Sven Groppe Tasneem Bano RehmanThis book constitutes the refereed conference proceedings of the 6th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2023, held in Lübeck, Germany, during November 9–10, 2023.The 17 full papers and 2 short papers presented in this book were carefully reviewed and selected from 76 submissions. The main conference AMLDA 2023 is renowned for presenting cutting-edge research on all aspects of machine learning as well as important application areas such as healthcare and medical imaging informatics, biometrics, forensics, precision agriculture, risk management, robotics, and satellite imaging.
Applied Machine Learning and High-Performance Computing on AWS: Accelerate the development of machine learning applications following architectural best practices
by Shreyas Subramanian Trenton Potgieter Mani Khanuja Farooq SabirBuild, train, and deploy large machine learning models at scale in various domains such as computational fluid dynamics, genomics, autonomous vehicles, and numerical optimization using Amazon SageMakerKey FeaturesUnderstand the need for high-performance computing (HPC)Build, train, and deploy large ML models with billions of parameters using Amazon SageMakerLearn best practices and architectures for implementing ML at scale using HPCBook DescriptionMachine learning (ML) and high-performance computing (HPC) on AWS run compute-intensive workloads across industries and emerging applications. Its use cases can be linked to various verticals, such as computational fluid dynamics (CFD), genomics, and autonomous vehicles.This book provides end-to-end guidance, starting with HPC concepts for storage and networking. It then progresses to working examples on how to process large datasets using SageMaker Studio and EMR. Next, you'll learn how to build, train, and deploy large models using distributed training. Later chapters also guide you through deploying models to edge devices using SageMaker and IoT Greengrass, and performance optimization of ML models, for low latency use cases.By the end of this book, you'll be able to build, train, and deploy your own large-scale ML application, using HPC on AWS, following industry best practices and addressing the key pain points encountered in the application life cycle.What you will learnExplore data management, storage, and fast networking for HPC applicationsFocus on the analysis and visualization of a large volume of data using SparkTrain visual transformer models using SageMaker distributed trainingDeploy and manage ML models at scale on the cloud and at the edgeGet to grips with performance optimization of ML models for low latency workloadsApply HPC to industry domains such as CFD, genomics, AV, and optimizationWho this book is forThe book begins with HPC concepts, however, it expects you to have prior machine learning knowledge. This book is for ML engineers and data scientists interested in learning advanced topics on using large datasets for training large models using distributed training concepts on AWS, deploying models at scale, and performance optimization for low latency use cases. Practitioners in fields such as numerical optimization, computation fluid dynamics, autonomous vehicles, and genomics, who require HPC for applying ML models to applications at scale will also find the book useful.
Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more
by Aditya BhattacharyaLeverage top XAI frameworks to explain your machine learning models with ease and discover best practices and guidelines to build scalable explainable ML systemsKey FeaturesExplore various explainability methods for designing robust and scalable explainable ML systemsUse XAI frameworks such as LIME and SHAP to make ML models explainable to solve practical problemsDesign user-centric explainable ML systems using guidelines provided for industrial applicationsBook DescriptionExplainable AI (XAI) is an emerging field that brings artificial intelligence (AI) closer to non-technical end users. XAI makes machine learning (ML) models transparent and trustworthy along with promoting AI adoption for industrial and research use cases.Applied Machine Learning Explainability Techniques comes with a unique blend of industrial and academic research perspectives to help you acquire practical XAI skills. You'll begin by gaining a conceptual understanding of XAI and why it's so important in AI. Next, you'll get the practical experience needed to utilize XAI in AI/ML problem-solving processes using state-of-the-art methods and frameworks. Finally, you'll get the essential guidelines needed to take your XAI journey to the next level and bridge the existing gaps between AI and end users.By the end of this ML book, you'll be equipped with best practices in the AI/ML life cycle and will be able to implement XAI methods and approaches using Python to solve industrial problems, successfully addressing key pain points encountered.What you will learnExplore various explanation methods and their evaluation criteriaLearn model explanation methods for structured and unstructured dataApply data-centric XAI for practical problem-solvingHands-on exposure to LIME, SHAP, TCAV, DALEX, ALIBI, DiCE, and othersDiscover industrial best practices for explainable ML systemsUse user-centric XAI to bring AI closer to non-technical end usersAddress open challenges in XAI using the recommended guidelinesWho this book is forThis book is for scientists, researchers, engineers, architects, and managers who are actively engaged in machine learning and related fields. Anyone who is interested in problem-solving using AI will benefit from this book. Foundational knowledge of Python, ML, DL, and data science is recommended. AI/ML experts working with data science, ML, DL, and AI will be able to put their knowledge to work with this practical guide. This book is ideal for you if you're a data and AI scientist, AI/ML engineer, AI/ML product manager, AI product owner, AI/ML researcher, and UX and HCI researcher.