Everybody wants to learn them. You're not a programmer though. How is a non technical user going to learn how to program? You do want to use macros to make your work easier but are you really going to sit down with a huge programming textbook and work your way through every. single. boring. page? Like most people, you'll start with great enthusiasm and vigor but after a few chapters, the novelty wears off. It gets boring. I'm going to try and change that and make learning macro programming entertaining and accessible to non-techies. First of all, programming Excel macros is a huge topic. Let's eat the elephant one bite at a time. Instead of sitting down with a dry, heavy text, you will read very focused, to the point topics. You can then immediately use what you learned in the real world. This is the first lesson in the series. You will learn what macros are, how to access them, a tiny bit of programming theory (just so you have a clue as to what's going on) and how to record macros. As with all my other lessons, this one has a follow along workbook that you can use to work through the exercises. The images in the lessons are based on Excel 2013 for Windows.

    It is suitable for a one or two semester university course on AI, as well as for researchers in the field.

    This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.

    Marieb and Katja Hoehn have produced the most accessible, comprehensive, up-to-date, and visually stunning anatomy & physiology textbook on the market. Marieb draws on her career as an A&P professor and her experience as a part-time nursing student, while Hoehn relies on her medical education and classroom experience to explain concepts and processes in a meaningful and memorable way. The most significant revision to date, the Eighth Edition makes it easier for you to learn key concepts in A&P. The new edition features a whole new art program that is not only more visually dynamic and vibrant than in previous editions but is also much more pedagogically effective for today's students, including new Focus figures, which guide you through the toughest concepts in A&P. The text has been edited to make it easier than ever to study from and navigate, with integrated objectives, new concept check questions, and a new design program.

    With plenty of engaging material, practice questions, and practical ideas, this updated edition contains everything your students need to succeed in Cambridge IGCSE biology.

    While these are essential, many young ecologists need to figure out how to actually do research themselves. How to Do Ecology provides nuts-and-bolts advice on how to develop a successful thesis and research program. This book presents different approaches to posing testable ecological questions. In particular, it covers the uses, strengths, and limitations of manipulative experiments in ecology. It will help young ecologists consider meaningful treatments, controls, replication, independence, and randomization in experiments, as well as where to do experiments and how to organize a season of work. This book also presents strategies for analyzing natural patterns, the value of alternative hypotheses, and what to do with negative results.Science is only part of being a successful ecologist. This engagingly written book offers students advice on working with other people and navigating their way through the land mines of research. Findings that don't get communicated are of little value. How to Do Ecology suggests effective ways to communicate information in the form of journal articles, oral presentations, and posters. Finally, it outlines strategies for developing successful grant and research proposals. Numerous checklists, figures, and boxes throughout the book summarize and reinforce the main points. In short, this book makes explicit many of the unspoken assumptions behind doing good research in ecology, and provides an invaluable resource for meaningful conversations among ecologists.

    With this concise book, you will learn how to use the latest version of this W3C standard to retrieve and manipulate the increasing amount of public and private data available via SPARQL endpoints. Several open source and commercial tools already support SPARQL, and this introduction gets you started right away.Begin with how to write and run simple SPARQL 1.1 queries, then dive into the language's powerful features and capabilities for manipulating the data you retrieve. Learn what you need to know to add to, update, and delete data in RDF datasets, and give web applications access to this data.Understand SPARQL’s connection with RDF, the semantic web, and related specificationsQuery and combine data from local and remote sourcesCopy, convert, and create new RDF dataLearn how datatype metadata, standardized functions, and extension functions contribute to your queriesIncorporate SPARQL queries into web-based applications

    This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

    Big data is on the tip of everyone's tongue. Everyone understands its power and importance, but many fail to grasp the actionable steps and resources required to utilise it effectively. This book fills the knowledge gap by showing how major companies are using big data every day, from an up-close, on-the-ground perspective. From technology, media and retail, to sport teams, government agencies and financial institutions, learn the actual strategies and processes being used to learn about customers, improve manufacturing, spur innovation, improve safety and so much more. Organised for easy dip-in navigation, each chapter follows the same structure to give you the information you need quickly. For each company profiled, learn what data was used, what problem it solved and the processes put it place to make it practical, as well as the technical details, challenges and lessons learned from each unique scenario. Learn how predictive analytics helps Amazon, Target, John Deere and Apple understand their customers Discover how big data is behind the success of Walmart, LinkedIn, Microsoft and more Learn how big data is changing medicine, law enforcement, hospitality, fashion, science and banking Develop your own big data strategy by accessing additional reading materials at the end of each chapter

    It covers the basics of computer programming, including variables and values, functions, conditionals and control flow, program development and debugging. Later chapters cover basic algorithms and data structures.

    The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

    The focus of this text is to solve real-world problems using the latest AI techniques. Intelligent systems like expert systems, fuzzy systems, artificial neural networks, genetic algorithms and ant colony systems are discussed in detail with case studies to facilitate in- depth understanding. Since the ultimate goal of AI is the construction of programs to solve problems, an entire chapter has been devoted to the programming languages used in AI problem solving. The theory is well supported by a large number of illustrations and end-chapter exercises. With its comprehensive coverage of the subject in a clear and concise manner this text would be extremely useful not only for undergraduate students, but also to postgraduate students.

    With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. You’ll explore the basic operations and common functions of Spark’s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Spark’s scalable machine-learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasets—Spark’s core APIs—through worked examples Dive into Spark’s low-level APIs, RDDs, and execution of SQL and DataFrames Understand how Spark runs on a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Structured Streaming, Spark’s stream-processing engine Learn how you can apply MLlib to a variety of problems, including classification or recommendation

    •Sample examples are given after topic for subject understanding. •Each chapter included “Topical Tests” to test the ability. •Important facts in the text have been highlighted in two colors. •“Additional solved examples” are provided at the end of the chapter. •Chapter Proficiency Test are covered given at the end of each chapter includes objective questions with multiple choice, previous years’ questions, single integer answer type, etc. •Hints & Solutions are provided at the end of every chapter with suitable figures, chemical reactions and formulas for understanding the chapter well.