52 resultater (0,61681 sekunder)

Vis alle filtre

Mærke

Butik

Pris (EUR)

Nulstil filter

Produkter
Fra
Butikker

Introducing Machine Learning - Francesco Esposito - Bog - Pearson Education (US) - Plusbog.dk

Introducing Machine Learning - Francesco Esposito - Bog - Pearson Education (US) - Plusbog.dk

Master machine learning concepts and develop real-world solutions Machine learning offers immense opportunities, and Introducing Machine Learning delivers practical knowledge to make the most of them. Dino and Francesco Esposito start with a quick overview of the foundations of artificial intelligence and the basic steps of any machine learning project. Next, they introduce Microsoft’s powerful ML.NET library, including capabilities for data processing, training, and evaluation. They present families of algorithms that can be trained to solve real-life problems, as well as deep learning techniques utilizing neural networks. The authors conclude by introducing valuable runtime services available through the Azure cloud platform and consider the long-term business vision for machine learning. · 14-time Microsoft MVP Dino Esposito and Francesco Esposito help you · Explore what’s known about how humans learn and how intelligent software is built · Discover which problems machine learning can address · Understand the machine learning pipeline: the steps leading to a deliverable model · Use AutoML to automatically select the best pipeline for any problem and dataset · Master ML.NET, implement its pipeline, and apply its tasks and algorithms · Explore the mathematical foundations of machine learning · Make predictions, improve decision-making, and apply probabilistic methods · Group data via classification and clustering · Learn the fundamentals of deep learning, including neural network design · Leverage AI cloud services to build better real-world solutions faster About This Book · For professionals who want to build machine learning applications: both developers who need data science skills and data scientists who need relevant programming skills · Includes examples of machine learning coding scenarios built using the ML.NET library

DKK 278.00
1

Machine Learning with Python for Everyone - Mark Fenner - Bog - Pearson Education (US) - Plusbog.dk

Machine Learning with Python for Everyone - Mark Fenner - Bog - Pearson Education (US) - Plusbog.dk

The Complete Beginner''s Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you''re an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning. Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you''ll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field''s most sophisticated and exciting techniques. Whether you''re a student, analyst, scientist, or hobbyist, this guide''s insights will be applicable to every learning system you ever build or use. - - Understand machine learning algorithms, models, and core machine learning concepts - - Classify examples with classifiers, and quantify examples with regressors - - Realistically assess performance of machine learning systems - - Use feature engineering to smooth rough data into useful forms - - Chain multiple components into one system and tune its performance - - Apply machine learning techniques to images and text - - Connect the core concepts to neural networks and graphical models - - Leverage the Python scikit-learn library and other powerful tools - Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

DKK 442.00
1

Just Enough Data Science and Machine Learning - Martyn Harris - Bog - Pearson Education (US) - Plusbog.dk

Just Enough Data Science and Machine Learning - Martyn Harris - Bog - Pearson Education (US) - Plusbog.dk

An accessible introduction to applied data science and machine learning, with minimal math and code required to master the foundational and technical aspects of data science. In Just Enough Data Science and Machine Learning, authors Mark Levene and Martyn Harris present a comprehensive and accessible introduction to data science. It allows the readers to develop an intuition behind the methods adopted in both data science and machine learning, which is the algorithmic component of data science involving the discovery of patterns from input data. This book looks at data science from an applied perspective, where emphasis is placed on the algorithmic aspects of data science and on the fundamental statistical concepts necessary to understand the subject. The book begins by exploring the nature of data science and its origins in basic statistics. The authors then guide readers through the essential steps of data science, starting with exploratory data analysis using visualisation tools. They explain the process of forming hypotheses, building statistical models, and utilising algorithmic methods to discover patterns in the data. Finally, the authors discuss general issues and preliminary concepts that are needed to understand machine learning, which is central to the discipline of data science. The book is packed with practical examples and real-world data sets throughout to reinforce the concepts. All examples are supported by Python code external to the reading material to keep the book timeless. Notable features of this book: Clear explanations of fundamental statistical notions and conceptsCoverage of various types of data and techniques for analysisIn-depth exploration of popular machine learning tools and methodsInsight into specific data science topics, such as social networks and sentiment analysisPractical examples and case studies for real-world applicationRecommended further reading for deeper exploration of specific topics.

DKK 382.00
1

Exam Ref DP-100 Designing and Implementing a Data Science Solution on Azure - Dayne Sorvisto - Bog - Pearson Education (US) - Plusbog.dk

Exam Ref DP-100 Designing and Implementing a Data Science Solution on Azure - Dayne Sorvisto - Bog - Pearson Education (US) - Plusbog.dk

Prepare for Microsoft Exam DP-100 and demonstrate your real-world knowledge of managing data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Python, Azure Machine Learning, and MLflow. Designed for professionals with data science experience, this Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the Microsoft Certified: Azure Data Scientist Associate level. Focus on the expertise measured by these objectives: - - Design and prepare a machine learning solution - - Explore data and train models - - Prepare a model for deployment - - Deploy and retrain a model - This Microsoft Exam Ref: - - Organizes its coverage by exam objectives - - Features strategic, what-if scenarios to challenge you - - Assumes you have experience in designing and creating a suitable working environment for data science workloads, training machine learning models, and managing, deploying, and monitoring scalable machine learning solutions - About the Exam Exam DP-100 focuses on knowledge needed to design and prepare a machine learning solution, manage an Azure Machine Learning workspace, explore data and train models, create models by using the Azure Machine Learning designer, prepare a model for deployment, manage models in Azure Machine Learning, deploy and retrain a model, and apply machine learning operations (MLOps) practices. About Microsoft Certification Passing this exam fulfills your requirements for the Microsoft Certified: Azure Data Scientist Associate credential, demonstrating your expertise in applying data science and machine learning to implement and run machine learning workloads on Azure, including knowledge and experience using Azure Machine Learning and MLflow.

DKK 398.00
1

Machine Learning in Production - Andrew Kelleher - Bog - Pearson Education (US) - Plusbog.dk

Machine Learning in Production - Andrew Kelleher - Bog - Pearson Education (US) - Plusbog.dk

The typical data science task in industry starts with an “ask” from the business. But few data scientists have been taught what to do with that ask. This book shows them how to assess it in the context of the business’s goals, reframe it to work optimally for both the data scientist and the employer, and then execute on it. Written by two of the experts who’ve achieved breakthrough optimizations at BuzzFeed, it’s packed with real-world examples that take you from start to finish: from ask to actionable insight. Andrew Kelleher and Adam Kelleher walk you through well-formed, concrete principles for approaching common data science problems, giving you an easy-to-use checklist for effective execution. Using their principles and techniques, you’ll gain deeper understanding of your data, learn how to analyze noise and confounding variables so they don’t compromise your analysis, and save weeks of iterative improvement by planning your projects more effectively upfront. Once students have mastered their principles, they will put those principles to work in two realistic, beginning-to-end site optimization tasks. These extended examples come complete with reusable code examples and recommended open-source solutions designed for easy adaptation to your everyday challenges. They will be especially valuable for anyone seeking their first data science job -- and everyone who’s found that job and wants to succeed in it.

DKK 344.00
1

Foundational Python for Data Science - Kennedy Behrman - Bog - Pearson Education (US) - Plusbog.dk

Foundational Python for Data Science - Kennedy Behrman - Bog - Pearson Education (US) - Plusbog.dk

Data science and machine learning—two of the world''s hottest fields—are attracting talent from a wide variety of technical, business, and liberal arts disciplines. Python, the world''s #1 programming language, is also the most popular language for data science and machine learning. This is the first guide specifically designed to help students with widely diverse backgrounds learn foundational Python so they can use it for data science and machine learning. This book is catered to introductory-level college courses on data science. Leading data science instructor and practitioner Kennedy Behrman first walks through the process of learning to code for the first time with Python and Jupyter notebook, then introduces key libraries every Python data science programmer needs to master. Once students have learned these foundations, Behrman introduces intermediate and applied Python techniques for real-world problem-solving. Throughout, Foundational Python for Data Science presents hands-on exercises, learning assessments, case studies, and more—all created with Colab (Jupyter compatible) notebooks, so students can execute all coding examples interactively without installing or configuring any software.

DKK 450.00
1

Programming ML.NET - Francesco Esposito - Bog - Pearson Education (US) - Plusbog.dk

Programming ML.NET - Francesco Esposito - Bog - Pearson Education (US) - Plusbog.dk

The expert guide to creating production machine learning solutions with ML.NET! ML.NET brings the power of machine learning to all .NET developers— and Programming ML.NET helps you apply it in real production solutions. Modeled on Dino Esposito’s best-selling Programming ASP.NET, this book takes the same scenario-based approach Microsoft’s team used to build ML.NET itself. After a foundational overview of ML.NET’s libraries, the authors illuminate mini-frameworks (“ML Tasks”) for regression, classification, ranking, anomaly detection, and more. For each ML Task, they offer insights for overcoming common real-world challenges. Finally, going far beyond shallow learning, the authors thoroughly introduce ML.NET neural networking. They present a complete example application demonstrating advanced Microsoft Azure cognitive services and a handmade custom Keras network— showing how to leverage popular Python tools within .NET. 14-time Microsoft MVP Dino Esposito and son Francesco Esposito show how to: - - Build smarter machine learning solutions that are closer to your user’s needs - - See how ML.NET instantiates the classic ML pipeline, and simplifies common scenarios such as sentiment analysis, fraud detection, and price prediction - - Implement data processing and training, and “productionize” machine learning–based software solutions - - Move from basic prediction to more complex tasks, including categorization, anomaly detection, recommendations, and image classification - - Perform both binary and multiclass classification - - Use clustering and unsupervised learning to organize data into homogeneous groups - - Spot outliers to detect suspicious behavior, fraud, failing equipment, or other issues - - Make the most of ML.NET’s powerful, flexible forecasting capabilities - - Implement the related functions of ranking, recommendation, and collaborative filtering - - Quickly build image classification solutions with ML.NET transfer learning - - Move to deep learning when standard algorithms and shallow learning aren’t enough - - “Buy” neural networking via the Azure Cognitive Services API, or explore building your own with Keras and TensorFlow -

DKK 368.00
1

Artificial Intelligence for Business - Doug Rose - Bog - Pearson Education (US) - Plusbog.dk

Artificial Intelligence for Business - Doug Rose - Bog - Pearson Education (US) - Plusbog.dk

The Easy Introduction to Machine Learning (Ml) for Nontechnical People--In Business and Beyond Artificial Intelligence for Business is your plain-English guide to Artificial Intelligence (AI) and Machine Learning (ML): how they work, what they can and cannot do, and how to start profiting from them. Writing for nontechnical executives and professionals, Doug Rose demystifies AI/ML technology with intuitive analogies and explanations honed through years of teaching and consulting. Rose explains everything from early “expert systems” to advanced deep learning networks. First, Rose explains how AI and ML emerged, exploring pivotal early ideas that continue to influence the field. Next, he deepens your understanding of key ML concepts, showing how machines can create strategies and learn from mistakes. Then, Rose introduces current powerful neural networks: systems inspired by the structure and function of the human brain. He concludes by introducing leading AI applications, from automated customer interactions to event prediction. Throughout, Rose stays focused on business: applying these technologies to leverage new opportunities and solve real problems. - - Compare the ways a machine can learn, and explore current leading ML algorithms - - Start with the right problems, and avoid common AI/ML project mistakes - - Use neural networks to automate decision-making and identify unexpected patterns - - Help neural networks learn more quickly and effectively - - Harness AI chatbots, virtual assistants, virtual agents, and conversational AI applications -

DKK 255.00
1

Pragmatic AI - Noah Gift - Bog - Pearson Education (US) - Plusbog.dk

Pragmatic AI - Noah Gift - Bog - Pearson Education (US) - Plusbog.dk

Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—even if you don’t have a strong background in math or data science. Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you’ll gain a more intuitive understanding of what you can achieve with them and how to maximize their value. Building on these fundamentals, you’ll walk step-by-step through building cloud-based AI/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether you’re a business professional, decision-maker, student, or programmer, Gift’s expert guidance and wide-ranging case studies will prepare you to solve data science problems in virtually any environment. - - Get and configure all the tools you’ll need - - Quickly review all the Python you need to start building machine learning applications - - Master the AI and ML toolchain and project lifecycle - - Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn - - Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems - - Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services - - Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more - - Work with Microsoft Azure AI APIs - - Walk through building six real-world AI applications, from start to finish - Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

DKK 317.00
1

Deep Learning Illustrated - Beyleveld Grant - Bog - Pearson Education (US) - Plusbog.dk

Deep Learning Illustrated - Beyleveld Grant - Bog - Pearson Education (US) - Plusbog.dk

Deep learning is one of today’s hottest fields. This approach to machine learning is achieving breakthrough results in some of today’s highest profile applications, in organizations ranging from Google to Tesla, Facebook to Apple. Thousands of technical professionals and students want to start leveraging its power, but previous books on deep learning have often been non-intuitive, inaccessible, and dry. In Deep Learning Illustrated , three world-class instructors and practitioners present a uniquely visual, intuitive, and accessible high-level introduction to the techniques and applications of deep learning. Packed with vibrant, full-color illustrations, it abstracts away much of the complexity of building deep learning models, making the field more fun to learn and accessible to a far wider audience. Part I’s high-level overview explains what Deep Learning is, why it has become so ubiquitous, and how it relates to concepts and terminology such as Artificial Intelligence, Machine Learning, Artificial Neural Networks, and Reinforcement Learning. These opening chapters are replete with vivid illustrations, easy-to-grasp analogies, and character-focused narratives. Building on this foundation, the authors then offer a practical reference and tutorial for applying a wide spectrum of proven deep learning techniques. Essential theory is covered with as little mathematics as possible and is illuminated with hands-on Python code. Theory is supported with practical "run-throughs" available in accompanying Jupyter notebooks, delivering a pragmatic understanding of all major deep learning approaches and their applications: machine vision, natural language processing, image generation, and videogaming.

DKK 433.00
1

MIPS Assembly Language Programming - Robert Britton - Bog - Pearson Education (US) - Plusbog.dk

MIPS Assembly Language Programming - Robert Britton - Bog - Pearson Education (US) - Plusbog.dk

For freshman/sophomore-level courses in Assembly Language Programming, Introduction to Computer Organization, and Introduction to Computer Architecture. Students using this text will gain an understanding of how the functional components of modern computers are put together and how a computer works at the machine language level. MIPS architecture embodies the fundamental design principles of all contemporary RISC architectures. By incorporating this text into their courses, instructors will be able to prepare their undergraduate students to go on to upper-division computer organization courses.

DKK 1014.00
1

Foundations of Deep Reinforcement Learning - Laura Graesser - Bog - Pearson Education (US) - Plusbog.dk

Foundations of Deep Reinforcement Learning - Laura Graesser - Bog - Pearson Education (US) - Plusbog.dk

In just a few years, deep reinforcement learning (DRL) systems such as DeepMinds DQN have yielded remarkable results. This hybrid approach to machine learning shares many similarities with human learning: its unsupervised self-learning, self-discovery of strategies, usage of memory, balance of exploration and exploitation, and its exceptional flexibility. Exciting in its own right, DRL may presage even more remarkable advances in general artificial intelligence. Deep Reinforcement Learning in Python: A Hands-On Introduction is the fastest and most accessible way to get started with DRL. The authors teach through practical hands-on examples presented with their advanced OpenAI Lab framework. While providing a solid theoretical overview, they emphasize building intuition for the theory, rather than a deep mathematical treatment of results. Coverage includes: - - Components of an RL system, including environment and agents - - Value-based algorithms: SARSA, Q-learning and extensions, offline learning - - Policy-based algorithms: REINFORCE and extensions; comparisons with value-based techniques - - Combined methods: Actor-Critic and extensions; scalability through async methods - - Agent evaluation - - Advanced and experimental techniques, and more - - - How to achieve breakthrough machine learning performance by combining deep neural networks with reinforcement learning - - Reduces the learning curve by relying on the authors’ OpenAI Lab framework: requires less upfront theory, math, and programming expertise - - Provides well-designed, modularized, and tested code examples with complete experimental data sets to illuminate the underlying algorithms - - Includes case studies, practical tips, definitions, and other aids to learning and mastery - - Prepares readers for exciting future advances in artificial general intelligence - The accessible, hands-on, full-color tutorial for building practical deep reinforcement learning solutions - - How to achieve breakthrough machine learning performance by combining deep neural networks with reinforcement learning - - Reduces the learning curve by relying on the authors’ OpenAI Lab framework: requires less upfront theory, math, and programming expertise - - Provides well-designed, modularized, and tested code examples with complete experimental data sets to illuminate the underlying algorithms - - Includes case studies, practical tips, definitions, and other aids to learning and mastery - - Prepares readers for exciting future advances in artificial general intelligence -

DKK 366.00
1

Exam Ref AI-900 Microsoft Azure AI Fundamentals - Julian Sharp - Bog - Pearson Education (US) - Plusbog.dk

Exam Ref AI-900 Microsoft Azure AI Fundamentals - Julian Sharp - Bog - Pearson Education (US) - Plusbog.dk

Prepare for Microsoft Exam AI-900 and help demonstrate your real-world knowledge of diverse machine learning (ML) and artificial intelligence (AI) workloads, and how they can be implemented with Azure AI. Designed for business stakeholders, new and existing IT professionals, consultants, and students, this Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the Microsoft Certified: Azure AI Fundamentals level. Focus on the expertise measured by these objectives: • Describe AI workloads and considerations • Describe fundamental principles of machine learning on Azure • Describe features of computer vision workloads on Azure • Describe features of Natural Language Processing (NLP) workloads on Azure • Describe features of conversational AI workloads on Azure This Microsoft Exam Ref: • Organizes its coverage by exam objectives • Features strategic, what-if scenarios to challenge you • Assumes you are a business user, stakeholder, technical professional, or student who wants to become familiar with Azure AI; requires no data science or software engineering experience. About the Exam Exam AI-900 focuses on knowledge needed to identify features of common AI workloads and guiding principles for responsible AI; identify common ML types; describe core ML concepts; identify core tasks in creating an ML solution; describe capabilities of no-code ML with Azure Machine Learning Studio; identify common types of computer vision solutions; identify Azure tools and services for computer vision tasks; identify features of common NLP workload scenarios; identify Azure tools and services for NLP workloads; and identify common use cases and Azure services for conversational Al. About Microsoft Certification Passing this exam fulfills your requirements for the Microsoft Certified: Azure AI Fundamentals certification, demonstrating your knowledge of common ML and AI workloads and how to implement them on Azure. With this certification, you can move on to earn more advanced role-based certifications, including Microsoft Certified: Azure AI Engineer Associate or Azure Data Scientist Associate. See full details at: microsoft.com/learn

DKK 291.00
1

Python for Programmers - Harvey Deitel - Bog - Pearson Education (US) - Plusbog.dk

Python for Programmers - Harvey Deitel - Bog - Pearson Education (US) - Plusbog.dk

The professional programmer''s Deitel® guide to Python® with introductory artificial intelligence case studies Written for programmers with a background in another high-level language, Python for Programmers uses hands-on instruction to teach today''s most compelling, leading-edge computing technologies and programming in Python--one of the world''s most popular and fastest-growing languages. Please read the Table of Contents diagram inside the front cover and the Preface for more details. In the context of 500+, real-world examples ranging from individual snippets to 40 large scripts and full implementation case studies, you''ll use the interactive IPython interpreter with code in Jupyter Notebooks to quickly master the latest Python coding idioms. After covering Python Chapters 1-5 and a few key parts of Chapters 6-7, you''ll be able to handle significant portions of the hands-on introductory AI case studies in Chapters 11-16, which are loaded with cool, powerful, contemporary examples. These include natural language processing, data mining Twitter® for sentiment analysis, cognitive computing with IBM® WatsonTM, supervised machine learning with classification and regression, unsupervised machine learning with clustering, computer vision through deep learning and convolutional neural networks, deep learning with recurrent neural networks, big data with Hadoop®, SparkTM and NoSQL databases, the Internet of Things and more. You''ll also work directly or indirectly with cloud-based services, including Twitter, Google TranslateTM, IBM Watson, Microsoft® Azure®, OpenMapQuest, PubNub and more. Features - - 500+ hands-on, real-world, live-code examples from snippets to case studies - - IPython + code in Jupyter® Notebooks - - Library-focused: Uses Python Standard Library and data science libraries to accomplish significant tasks with minimal code - - Rich Python coverage: Control statements, functions, strings, files, JSON serialization, CSV, exceptions - - Procedural, functional-style and object-oriented programming - - Collections: Lists, tuples, dictionaries, sets, NumPy arrays, pandas Series & DataFrames - - Static, dynamic and interactive visualizations - - Data experiences with real-world datasets and data sources - - Intro to Data Science sections: AI, basic stats, simulation, animation, random variables, data wrangling, regression - - AI, big data and cloud data science case studies: NLP, data mining Twitter®, IBM® WatsonTM, machine learning, deep learning, computer vision, Hadoop®, SparkTM, NoSQL, IoT - - Open-source libraries: NumPy, pandas, Matplotlib, Seaborn, Folium, SciPy, NLTK, TextBlob, spaCy, Textatistic, Tweepy, scikit-learn®, Keras and more - Register your product to gain access to updated chapters and material, as well as downloads, future updates, and/or corrections as they become available. See inside book for more information.

DKK 450.00
1

Kollected Kode Vicious, The - George Neville Neil - Bog - Pearson Education (US) - Plusbog.dk

Kollected Kode Vicious, The - George Neville Neil - Bog - Pearson Education (US) - Plusbog.dk

Pragmatic, Bite-Sized Programming Advice from Koder-with-Attitude, Kode Vicious “For many years I have been a fan of the regular columns by Kode Vicious in Communications of the ACM . The topics are not only timely, they''re explained with wit and elegance.” --From the Foreword by Donald E. Knuth Writing as Kode Vicious (KV), George V. Neville-Neil has spent more than 15 years sharing incisive advice and fierce insights for everyone who codes, works with code, or works with coders. Now, in The Kollected Kode Vicious , he has brought together his best essays and Socratic dialogues on the topic of building more effective computer systems. These columns have been among the most popular items published in ACM Queue magazine, as well as Communications of the ACM , and KV''s entertaining and perceptive explorations are supplemented here with new material that illuminates broader themes and addresses issues relevant to every software professional. Neville-Neil cuts to the heart of the matter and offers practical takeaways for newcomers and veterans alike on the following topics: - - The Kode at Hand: What to do (or not to do) with a specific piece of code - - Koding Konundrums: Issues that surround code, such as testing and documentation - - Systems Design: Overall systems design topics, from abstraction and threads to security - - Machine to Machine: Distributed systems and computer networking - - Human to Human: Dealing with developers, managers, and other people - Each chapter brings together letters, responses, and advice that apply directly to day-to-day problems faced by those who work in or with computing systems. While the answers to the questions posed are always written with an eye towards humor, the advice given is deadly serious. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

DKK 344.00
1

Practical Data Science with Hadoop and Spark - Casey Stella - Bog - Pearson Education (US) - Plusbog.dk

Practical Data Science with Hadoop and Spark - Casey Stella - Bog - Pearson Education (US) - Plusbog.dk

The Complete Guide to Data Science with Hadoop—For Technical Professionals, Businesspeople, and Students Demand is soaring for professionals who can solve real data science problems with Hadoop and Spark. Practical Data Science with Hadoop® and Spark is your complete guide to doing just that. Drawing on immense experience with Hadoop and big data, three leading experts bring together everything you need: high-level concepts, deep-dive techniques, real-world use cases, practical applications, and hands-on tutorials. The authors introduce the essentials of data science and the modern Hadoop ecosystem, explaining how Hadoop and Spark have evolved into an effective platform for solving data science problems at scale. In addition to comprehensive application coverage, the authors also provide useful guidance on the important steps of data ingestion, data munging, and visualization. Once the groundwork is in place, the authors focus on specific applications, including machine learning, predictive modeling for sentiment analysis, clustering for document analysis, anomaly detection, and natural language processing (NLP). This guide provides a strong technical foundation for those who want to do practical data science, and also presents business-driven guidance on how to apply Hadoop and Spark to optimize ROI of data science initiatives. Learn - - What data science is, how it has evolved, and how to plan a data science career - - How data volume, variety, and velocity shape data science use cases - - Hadoop and its ecosystem, including HDFS, MapReduce, YARN, and Spark - - Data importation with Hive and Spark - - Data quality, preprocessing, preparation, and modeling - - Visualization: surfacing insights from huge data sets - - Machine learning: classification, regression, clustering, and anomaly detection - - Algorithms and Hadoop tools for predictive modeling - - Cluster analysis and similarity functions - - Large-scale anomaly detection - - NLP: applying data science to human language -

DKK 348.00
1

Sources of the West, Volume 1 - Mark Kishlansky - Bog - Pearson Education (US) - Plusbog.dk

Sources of the West, Volume 1 - Mark Kishlansky - Bog - Pearson Education (US) - Plusbog.dk

Read the voices of the past to connect with the present. For introductory courses in western civilization. Kishlansky presents a well-balanced selection of readings that integrate coverage of social, economic, religious and cultural history within a traditional, political framework. Sources of the West includes documents on political theory, philosophy, imaginative literature and social history as well as constitutional documents, all of which raise significant issues for classroom discussions or lectures. By reading the voices of the past, students can connect them to the present and learn to understand and respect other cultures while thinking critically about history. Teaching and Learning Experience Personalize Learning - MySearchLab provides engaging experiences that personalize learning and comes from a trusted partner with educational expertise and a deep commitment to helping students and instructors achieve their goals. Improve Critical Thinking- An introductory How to Read a Document essay provides students with a road map of how to approach and analyze each selection. The essay explains the types and levels of questions students need to ask and answer in order to understand each document. Engage Students- Each selection raises a significant issue around which classroom discussion can take place or to which lectures can refer. Some may even inspire students to seek out the complete original works. Support Instructors- MySearchLab and ClassPrep.

DKK 812.00
1

Apache Spark in 24 Hours, Sams Teach Yourself - Jeffrey Aven - Bog - Pearson Education (US) - Plusbog.dk

Apache Spark in 24 Hours, Sams Teach Yourself - Jeffrey Aven - Bog - Pearson Education (US) - Plusbog.dk

Apache Spark is a fast, scalable, and flexible open source distributed processing engine for big data systems and is one of the most active open source big data projects to date. In just 24 lessons of one hour or less, Sams Teach Yourself Apache Spark in 24 Hours helps you build practical Big Data solutions that leverage Spark’s amazing speed, scalability, simplicity, and versatility. This book’s straightforward, step-by-step approach shows you how to deploy, program, optimise, manage, integrate, and extend Spark–now, and for years to come. You’ll discover how to create powerful solutions encompassing cloud computing, real-time stream processing, machine learning, and more. Every lesson builds on what you’ve already learned, giving you a rock-solid foundation for real-world success. Whether you are a data analyst, data engineer, data scientist, or data steward, learning Spark will help you to advance your career or embark on a new career in the booming area of Big Data. Learn how to - - Discover what Apache Spark does and how it fits into the Big Data landscape - - Deploy and run Spark locally or in the cloud - - Interact with Spark from the shell - - Make the most of the Spark Cluster Architecture - - Develop Spark applications with Scala and functional Python - - Program with the Spark API, including transformations and actions - - Apply practical data engineering/analysis approaches designed for Spark - - Use Resilient Distributed Datasets (RDDs) for caching, persistence, and output - - Optimise Spark solution performance - - Use Spark with SQL (via Spark SQL) and with NoSQL (via Cassandra) - - Leverage cutting-edge functional programming techniques - - Extend Spark with streaming, R, and Sparkling Water - - Start building Spark-based machine learning and graph-processing applications - - Explore advanced messaging technologies, including Kafka - - Preview and prepare for Spark’s next generation of innovations - Instructions walk you through common questions, issues, and tasks; Q-and-As, Quizzes, and Exercises build and test your knowledge; "Did You Know?" tips offer insider advice and shortcuts; and "Watch Out!" alerts help you avoid pitfalls. By the time you''re finished, you''ll be comfortable using Apache Spark to solve a wide spectrum of Big Data problems.

DKK 322.00
1

Pandas for Everyone - Daniel Chen - Bog - Pearson Education (US) - Plusbog.dk

Pandas for Everyone - Daniel Chen - Bog - Pearson Education (US) - Plusbog.dk

Manage and Automate Data Analysis with Pandas in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple data sets. Pandas for Everyone, 2nd Edition, brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world data science problems such as using regularization to prevent data overfitting, or when to use unsupervised machine learning methods to find the underlying structure in a data set.New features to the second edition include: - - Extended coverage of plotting and the seaborn data visualization library - - Expanded examples and resources - - Updated Python 3.9 code and packages coverage, including statsmodels and scikit-learn libraries - - Online bonus material on geopandas, Dask, and creating interactive graphics with Altair - Chen gives you a jumpstart on using Pandas with a realistic data set and covers combining data sets, handling missing data, and structuring data sets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes.Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability and introduces you to the wider Python data analysis ecosystem. - - Work with DataFrames and Series, and import or export data - - Create plots with matplotlib, seaborn, and pandas - - Combine data sets and handle missing data - - Reshape, tidy, and clean data sets so they’re easier to work with - - Convert data types and manipulate text strings - - Apply functions to scale data manipulations - - Aggregate, transform, and filter large data sets with groupby - - Leverage Pandas’ advanced date and time capabilities - - Fit linear models using statsmodels and scikit-learn libraries - - Use generalized linear modeling to fit models with different response variables - - Compare multiple models to select the “best” one - - Regularize to overcome overfitting and improve performance - - Use clustering in unsupervised machine learning -

DKK 419.00
1

The AI Revolution in Customer Service and Support - Emily Mckeon - Bog - Pearson Education (US) - Plusbog.dk

The AI Revolution in Customer Service and Support - Emily Mckeon - Bog - Pearson Education (US) - Plusbog.dk

In the rapidly evolving AI landscape, customer service and support professionals find themselves in a prime position to take advantage of this innovative technology to drive customer success. The AI Revolution in Customer Service and Support is a practical guide for professionals who want to harness the power of generative AI within their organizations to create more powerful customer and employee experiences. This book is designed to equip you with the knowledge and confidence to embrace the AI revolution and integrate the technology, such as large language models (LLMs), machine learning, predictive analytics, and gamified learning, into the customer experience. Start your journey toward leveraging this technology effectively to optimize organizational productivity. A portion of the book’s proceeds will be donated to the nonprofit Future World Alliance, dedicated to K-12 AI ethics education. IN THIS BOOK YOU’LL LEARN - - About AI, machine learning, and data science - - How to develop an AI vision for your organization - - How and where to incorporate AI technology in your customer experience fl ow - - About new roles and responsibilities for your organization - - How to improve customer experience while optimizing productivity - - How to implement responsible AI practices - - How to strengthen your culture across all generations in the workplace - - How to address concerns and build strategies for reskilling and upskilling your people - - How to incorporate games, play, and other techniques to engage your agents with AI - - Explore thought experiments for the future of support in your organization - “Insightful & comprehensive—if you run a service & support operation, put this book on your essential reading list right now!” —PHIL WOLFENDEN, Cisco, VP, Customer Experience “This book is both timely and relevant as we enter an unprecedented period in our industry and the broader world driven by Generative AI. The magnitude and speed of change we’re experiencing is astounding and this book does an outstanding job balancing technical knowledge with the people and ethical considerations we must also keep front of mind.” —BRYAN BELMONT, Microsoft, Corporate VP, Customer Service & Support “The authors of this book are undoubtedly on the front lines of operationalizing Gen AI implementations in customer support environments… and they know undoubtedly that at its core, support is about people and genuine human connections. This book walks you through their journey to keep people at the center of this technical tsunami.” —PHAEDRA BOINODIRIS, Author, AI for the Rest of Us

DKK 316.00
1

Blown to Bits - Wendy Seltzer - Bog - Pearson Education (US) - Plusbog.dk

Blown to Bits - Wendy Seltzer - Bog - Pearson Education (US) - Plusbog.dk

What you must know to protect yourself today The digital technology explosion has blown everything to bits--and the blast has provided new challenges and opportunities. This second edition of Blown to Bit s delivers the knowledge you need to take greater control of your information environment and thrive in a world that''s coming whether you like it or not. Straight from internationally respected Harvard/MIT experts, this plain-English bestseller has been fully revised for the latest controversies over social media, “fake news,” big data, cyberthreats, privacy, artificial intelligence and machine learning, self-driving cars, the Internet of Things, and much more. - - Discover who owns all that data about you—and what they can infer from it - - Learn to challenge algorithmic decisions - - See how close you can get to sending truly secure messages - - Decide whether you really want always-on cameras and microphones - - Explore the realities of Internet free speech - - Protect yourself against out-of-control technologies (and the powerful organizations that wield them) - You will find clear explanations, practical examples, and real insight into what digital tech means to you--as an individual, and as a citizen.

DKK 272.00
1

Data Cleaning for Effective Data Science - David Mertz - Bog - Pearson Education (US) - Plusbog.dk

Data Cleaning for Effective Data Science - David Mertz - Bog - Pearson Education (US) - Plusbog.dk

Most machine learning guides cover data cleaning briefly or skip it entirely. However, many data scientists and analysts spend most of their time on data cleaning and data quality tasks, and their effectiveness can make or break project success. In Data Cleaning for Effective Data Science , leading data science trainer David Mertz provides the most systematic guide to cleaning data for any project, using any library or toolset. Mertz introduces many powerful techniques for analyzing, manipulating, and pre-processing data sources. He offers best practices for working with leading data formats such as JSON, CSV, SQL RDBMSes, HDF5, NoSQL databases, files in image formats, binary serialized data structures, and more. Mertz also focuses on crucial issues within the data itself, including missing data, outliers, biasing trends, class imbalance, value imputation, over/under-sampling, normalization and/or randomization, and anomalies. This guide is organized around downloadable datasets, each illuminating specific issues with data integrity or quality. Each chapter explores the best ways to diagnose, analyze, and remediate these issues, offering hands-on practice using tools such as Python, Pandas, sklearn.preprocessing, scipy.stats, R, and Tidyverse. While the examples are demonstrated with widely-used tools, Mertz''s concepts are applicable with any toolset. Each chapter also links to additional datasets with more problems, exercises, and solutions.

DKK 388.00
1