Practical Machine Learning A New Look At Anomaly Detection

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Practical Machine Learning: A New Look at Anomaly Detection

Author : Ted Dunning
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491914181
Total Pages : 65 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Practical Machine Learning: A New Look at Anomaly Detection by : Ted Dunning

Download or read book Practical Machine Learning: A New Look at Anomaly Detection written by Ted Dunning and published by "O'Reilly Media, Inc.". This book was released on 2014-07-21 with total page 65 pages. Available in PDF, EPUB and Kindle. Book excerpt: Finding Data Anomalies You Didn't Know to Look For Anomaly detection is the detective work of machine learning: finding the unusual, catching the fraud, discovering strange activity in large and complex datasets. But, unlike Sherlock Holmes, you may not know what the puzzle is, much less what “suspects” you’re looking for. This O’Reilly report uses practical examples to explain how the underlying concepts of anomaly detection work. From banking security to natural sciences, medicine, and marketing, anomaly detection has many useful applications in this age of big data. And the search for anomalies will intensify once the Internet of Things spawns even more new types of data. The concepts described in this report will help you tackle anomaly detection in your own project. Use probabilistic models to predict what’s normal and contrast that to what you observe Set an adaptive threshold to determine which data falls outside of the normal range, using the t-digest algorithm Establish normal fluctuations in complex systems and signals (such as an EKG) with a more adaptive probablistic model Use historical data to discover anomalies in sporadic event streams, such as web traffic Learn how to use deviations in expected behavior to trigger fraud alerts

Network Anomaly Detection

Author : Dhruba Kumar Bhattacharyya
Publisher : CRC Press
ISBN 13 : 146658209X
Total Pages : 364 pages
Book Rating : 4.4/5 (665 download)

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Book Synopsis Network Anomaly Detection by : Dhruba Kumar Bhattacharyya

Download or read book Network Anomaly Detection written by Dhruba Kumar Bhattacharyya and published by CRC Press. This book was released on 2013-06-18 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the rapid rise in the ubiquity and sophistication of Internet technology and the accompanying growth in the number of network attacks, network intrusion detection has become increasingly important. Anomaly-based network intrusion detection refers to finding exceptional or nonconforming patterns in network traffic data compared to normal behavi

Practical Machine Learning for Computer Vision

Author : Valliappa Lakshmanan
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1098102339
Total Pages : 481 pages
Book Rating : 4.0/5 (981 download)

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Book Synopsis Practical Machine Learning for Computer Vision by : Valliappa Lakshmanan

Download or read book Practical Machine Learning for Computer Vision written by Valliappa Lakshmanan and published by "O'Reilly Media, Inc.". This book was released on 2021-07-21 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras. You'll learn how to: Design ML architecture for computer vision tasks Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model Preprocess images for data augmentation and to support learnability Incorporate explainability and responsible AI best practices Deploy image models as web services or on edge devices Monitor and manage ML models

Real-World Hadoop

Author : Ted Dunning
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491928921
Total Pages : 104 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Real-World Hadoop by : Ted Dunning

Download or read book Real-World Hadoop written by Ted Dunning and published by "O'Reilly Media, Inc.". This book was released on 2015-03-24 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you’re a business team leader, CIO, business analyst, or developer interested in how Apache Hadoop and Apache HBase-related technologies can address problems involving large-scale data in cost-effective ways, this book is for you. Using real-world stories and situations, authors Ted Dunning and Ellen Friedman show Hadoop newcomers and seasoned users alike how NoSQL databases and Hadoop can solve a variety of business and research issues. You’ll learn about early decisions and pre-planning that can make the process easier and more productive. If you’re already using these technologies, you’ll discover ways to gain the full range of benefits possible with Hadoop. While you don’t need a deep technical background to get started, this book does provide expert guidance to help managers, architects, and practitioners succeed with their Hadoop projects. Examine a day in the life of big data: India’s ambitious Aadhaar project Review tools in the Hadoop ecosystem such as Apache’s Spark, Storm, and Drill to learn how they can help you Pick up a collection of technical and strategic tips that have helped others succeed with Hadoop Learn from several prototypical Hadoop use cases, based on how organizations have actually applied the technology Explore real-world stories that reveal how MapR customers combine use cases when putting Hadoop and NoSQL to work, including in production

Mathematics for Machine Learning

Author : Marc Peter Deisenroth
Publisher : Cambridge University Press
ISBN 13 : 1108569323
Total Pages : 392 pages
Book Rating : 4.1/5 (85 download)

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Book Synopsis Mathematics for Machine Learning by : Marc Peter Deisenroth

Download or read book Mathematics for Machine Learning written by Marc Peter Deisenroth and published by Cambridge University Press. This book was released on 2020-04-23 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

Beginning Anomaly Detection Using Python-Based Deep Learning

Author : Sridhar Alla
Publisher : Apress
ISBN 13 : 1484251776
Total Pages : 427 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Beginning Anomaly Detection Using Python-Based Deep Learning by : Sridhar Alla

Download or read book Beginning Anomaly Detection Using Python-Based Deep Learning written by Sridhar Alla and published by Apress. This book was released on 2019-10-10 with total page 427 pages. Available in PDF, EPUB and Kindle. Book excerpt: Utilize this easy-to-follow beginner's guide to understand how deep learning can be applied to the task of anomaly detection. Using Keras and PyTorch in Python, the book focuses on how various deep learning models can be applied to semi-supervised and unsupervised anomaly detection tasks. This book begins with an explanation of what anomaly detection is, what it is used for, and its importance. After covering statistical and traditional machine learning methods for anomaly detection using Scikit-Learn in Python, the book then provides an introduction to deep learning with details on how to build and train a deep learning model in both Keras and PyTorch before shifting the focus to applications of the following deep learning models to anomaly detection: various types of Autoencoders, Restricted Boltzmann Machines, RNNs & LSTMs, and Temporal Convolutional Networks. The book explores unsupervised and semi-supervised anomaly detection along with the basics of time series-based anomaly detection. By the end of the book you will have a thorough understanding of the basic task of anomaly detection as well as an assortment of methods to approach anomaly detection, ranging from traditional methods to deep learning. Additionally, you are introduced to Scikit-Learn and are able to create deep learning models in Keras and PyTorch. What You Will LearnUnderstand what anomaly detection is and why it is important in today's world Become familiar with statistical and traditional machine learning approaches to anomaly detection using Scikit-Learn Know the basics of deep learning in Python using Keras and PyTorch Be aware of basic data science concepts for measuring a model's performance: understand what AUC is, what precision and recall mean, and more Apply deep learning to semi-supervised and unsupervised anomaly detection Who This Book Is For Data scientists and machine learning engineers interested in learning the basics of deep learning applications in anomaly detection

Practical Machine Learning with Python

Author : Dipanjan Sarkar
Publisher : Apress
ISBN 13 : 1484232070
Total Pages : 545 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Practical Machine Learning with Python by : Dipanjan Sarkar

Download or read book Practical Machine Learning with Python written by Dipanjan Sarkar and published by Apress. This book was released on 2017-12-20 with total page 545 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully. Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries and frameworks are also covered. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. You will learn how to process, wrangle, summarize and visualize data in its various forms. Feature engineering and selection methodologies will be covered in detail with real-world datasets followed by model building, tuning, interpretation and deployment. Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem. Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today! What You'll Learn Execute end-to-end machine learning projects and systems Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a wide range of machine learning models including regression, classification, and clustering. Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning. Who This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students

Intelligent Distributed Computing XI

Author : Mirjana Ivanović
Publisher : Springer
ISBN 13 : 3319663798
Total Pages : 319 pages
Book Rating : 4.3/5 (196 download)

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Book Synopsis Intelligent Distributed Computing XI by : Mirjana Ivanović

Download or read book Intelligent Distributed Computing XI written by Mirjana Ivanović and published by Springer. This book was released on 2017-10-03 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a collection of contributions addressing recent advances and research in synergistic combinations of topics in the joint fields of intelligent computing and distributed computing. It focuses on the following specific topics: distributed data mining and machine learning, reasoning and decision-making in distributed environments, distributed evolutionary algorithms, trust and reputation models for distributed systems, scheduling and resource allocation in distributed systems, intelligent multi-agent systems, advanced agent-based and service-based architectures, and Smart Cloud and Internet of Things (IoT) environments. The book represents the combined peer-reviewed proceedings of the 11th International Symposium on Intelligent Distributed Computing (IDC 2017) and the 7th International Workshop on Applications of Software Agents (WASA 2017), both of which were held in Belgrade, Serbia from October 11 to 13, 2017.

Data Analytics

Author : Juan J. Cuadrado-Gallego
Publisher : Springer Nature
ISBN 13 : 3031391292
Total Pages : 486 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis Data Analytics by : Juan J. Cuadrado-Gallego

Download or read book Data Analytics written by Juan J. Cuadrado-Gallego and published by Springer Nature. This book was released on 2023-11-30 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt: Building upon the knowledge introduced in The Data Science Framework, this book provides a comprehensive and detailed examination of each aspect of Data Analytics, both from a theoretical and practical standpoint. The book explains representative algorithms associated with different techniques, from their theoretical foundations to their implementation and use with software tools. Designed as a textbook for a Data Analytics Fundamentals course, it is divided into seven chapters to correspond with 16 weeks of lessons, including both theoretical and practical exercises. Each chapter is dedicated to a lesson, allowing readers to dive deep into each topic with detailed explanations and examples. Readers will learn the theoretical concepts and then immediately apply them to practical exercises to reinforce their knowledge. And in the lab sessions, readers will learn the ins and outs of the R environment and data science methodology to solve exercises with the R language. With detailed solutions provided for all examples and exercises, readers can use this book to study and master data analytics on their own. Whether you're a student, professional, or simply curious about data analytics, this book is a must-have for anyone looking to expand their knowledge in this exciting field.

Streaming Architecture

Author : Ted Dunning
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491953888
Total Pages : 116 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Streaming Architecture by : Ted Dunning

Download or read book Streaming Architecture written by Ted Dunning and published by "O'Reilly Media, Inc.". This book was released on 2016-05-10 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: More and more data-driven companies are looking to adopt stream processing and streaming analytics. With this concise ebook, you’ll learn best practices for designing a reliable architecture that supports this emerging big-data paradigm. Authors Ted Dunning and Ellen Friedman (Real World Hadoop) help you explore some of the best technologies to handle stream processing and analytics, with a focus on the upstream queuing or message-passing layer. To illustrate the effectiveness of these technologies, this book also includes specific use cases. Ideal for developers and non-technical people alike, this book describes: Key elements in good design for streaming analytics, focusing on the essential characteristics of the messaging layer New messaging technologies, including Apache Kafka and MapR Streams, with links to sample code Technology choices for streaming analytics: Apache Spark Streaming, Apache Flink, Apache Storm, and Apache Apex How stream-based architectures are helpful to support microservices Specific use cases such as fraud detection and geo-distributed data streams Ted Dunning is Chief Applications Architect at MapR Technologies, and active in the open source community. He currently serves as VP for Incubator at the Apache Foundation, as a champion and mentor for a large number of projects, and as committer and PMC member of the Apache ZooKeeper and Drill projects. Ted is on Twitter as @ted_dunning. Ellen Friedman, a committer for the Apache Drill and Apache Mahout projects, is a solutions consultant and well-known speaker and author, currently writing mainly about big data topics. With a PhD in Biochemistry, she has years of experience as a research scientist and has written about a variety of technical topics. Ellen is on Twitter as @Ellen_Friedman.

Sharing Big Data Safely

Author : Ted Dunning
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491953632
Total Pages : 95 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Sharing Big Data Safely by : Ted Dunning

Download or read book Sharing Big Data Safely written by Ted Dunning and published by "O'Reilly Media, Inc.". This book was released on 2015-09-15 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many big data-driven companies today are moving to protect certain types of data against intrusion, leaks, or unauthorized eyes. But how do you lock down data while granting access to people who need to see it? In this practical book, authors Ted Dunning and Ellen Friedman offer two novel and practical solutions that you can implement right away. Ideal for both technical and non-technical decision makers, group leaders, developers, and data scientists, this book shows you how to: Share original data in a controlled way so that different groups within your organization only see part of the whole. You’ll learn how to do this with the new open source SQL query engine Apache Drill. Provide synthetic data that emulates the behavior of sensitive data. This approach enables external advisors to work with you on projects involving data that you can't show them. If you’re intrigued by the synthetic data solution, explore the log-synth program that Ted Dunning developed as open source code (available on GitHub), along with how-to instructions and tips for best practice. You’ll also get a collection of use cases. Providing lock-down security while safely sharing data is a significant challenge for a growing number of organizations. With this book, you’ll discover new options to share data safely without sacrificing security.

Data Science For Cyber-security

Author : Nicholas A Heard
Publisher : World Scientific
ISBN 13 : 178634565X
Total Pages : 305 pages
Book Rating : 4.7/5 (863 download)

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Book Synopsis Data Science For Cyber-security by : Nicholas A Heard

Download or read book Data Science For Cyber-security written by Nicholas A Heard and published by World Scientific. This book was released on 2018-09-26 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cyber-security is a matter of rapidly growing importance in industry and government. This book provides insight into a range of data science techniques for addressing these pressing concerns.The application of statistical and broader data science techniques provides an exciting growth area in the design of cyber defences. Networks of connected devices, such as enterprise computer networks or the wider so-called Internet of Things, are all vulnerable to misuse and attack, and data science methods offer the promise to detect such behaviours from the vast collections of cyber traffic data sources that can be obtained. In many cases, this is achieved through anomaly detection of unusual behaviour against understood statistical models of normality.This volume presents contributed papers from an international conference of the same name held at Imperial College. Experts from the field have provided their latest discoveries and review state of the art technologies.

ECML PKDD 2020 Workshops

Author : Irena Koprinska
Publisher : Springer Nature
ISBN 13 : 3030659658
Total Pages : 619 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis ECML PKDD 2020 Workshops by : Irena Koprinska

Download or read book ECML PKDD 2020 Workshops written by Irena Koprinska and published by Springer Nature. This book was released on 2021-02-01 with total page 619 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the refereed proceedings of the workshops which complemented the 20th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2020. Due to the COVID-19 pandemic the conference and workshops were held online. The 43 papers presented in volume were carefully reviewed and selected from numerous submissions. The volume presents the papers that have been accepted for the following workshops: 5th Workshop on Data Science for Social Good, SoGood 2020; Workshop on Parallel, Distributed and Federated Learning, PDFL 2020; Second Workshop on Machine Learning for Cybersecurity, MLCS 2020, 9th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2020, Workshop on Data Integration and Applications, DINA 2020, Second Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning, EDML 2020, Second International Workshop on eXplainable Knowledge Discovery in Data Mining, XKDD 2020; 8th International Workshop on News Recommendation and Analytics, INRA 2020. The papers from INRA 2020 are published open access and licensed under the terms of the Creative Commons Attribution 4.0 International License.

Practical Machine Learning with R

Author : Brindha Priyadarshini Jeyaraman
Publisher : Packt Publishing Ltd
ISBN 13 : 1838552847
Total Pages : 416 pages
Book Rating : 4.8/5 (385 download)

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Book Synopsis Practical Machine Learning with R by : Brindha Priyadarshini Jeyaraman

Download or read book Practical Machine Learning with R written by Brindha Priyadarshini Jeyaraman and published by Packt Publishing Ltd. This book was released on 2019-08-30 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understand how machine learning works and get hands-on experience of using R to build algorithms that can solve various real-world problems Key FeaturesGain a comprehensive overview of different machine learning techniquesExplore various methods for selecting a particular algorithmImplement a machine learning project from problem definition through to the final modelBook Description With huge amounts of data being generated every moment, businesses need applications that apply complex mathematical calculations to data repeatedly and at speed. With machine learning techniques and R, you can easily develop these kinds of applications in an efficient way. Practical Machine Learning with R begins by helping you grasp the basics of machine learning methods, while also highlighting how and why they work. You will understand how to get these algorithms to work in practice, rather than focusing on mathematical derivations. As you progress from one chapter to another, you will gain hands-on experience of building a machine learning solution in R. Next, using R packages such as rpart, random forest, and multiple imputation by chained equations (MICE), you will learn to implement algorithms including neural net classifier, decision trees, and linear and non-linear regression. As you progress through the book, you’ll delve into various machine learning techniques for both supervised and unsupervised learning approaches. In addition to this, you’ll gain insights into partitioning the datasets and mechanisms to evaluate the results from each model and be able to compare them. By the end of this book, you will have gained expertise in solving your business problems, starting by forming a good problem statement, selecting the most appropriate model to solve your problem, and then ensuring that you do not overtrain it. What you will learnDefine a problem that can be solved by training a machine learning modelObtain, verify and clean data before transforming it into the correct format for usePerform exploratory analysis and extract features from dataBuild models for neural net, linear and non-linear regression, classification, and clusteringEvaluate the performance of a model with the right metricsImplement a classification problem using the neural net packageEmploy a decision tree using the random forest libraryWho this book is for If you are a data analyst, data scientist, or a business analyst who wants to understand the process of machine learning and apply it to a real dataset using R, this book is just what you need. Data scientists who use Python and want to implement their machine learning solutions using R will also find this book very useful. The book will also enable novice programmers to start their journey in data science. Basic knowledge of any programming language is all you need to get started.

Servitization and Physical Asset Management

Author : Michael John Provost
Publisher : SAE International
ISBN 13 : 0768094860
Total Pages : 328 pages
Book Rating : 4.7/5 (68 download)

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Book Synopsis Servitization and Physical Asset Management by : Michael John Provost

Download or read book Servitization and Physical Asset Management written by Michael John Provost and published by SAE International. This book was released on 2018-12-31 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Servitization and Physical Asset Management, third edition, was developed to provide a structured source of guidance and reference information on the business opportunities linked to servitization and the management of physical assets. A growing trend in the global economy, servitization focuses on the actual deliverables of an asset from the perspective of the customer: electricity instead of the power plant, thrust instead of the engine, mobility instead of a plane or a car. The book offers high-level overviews of how to servitized and manage assets from a variety of perspectives, reviewing nearly 1,500 books, magazine articles, papers and presentations and websites. Written by Michael J. Provost, Ph.D., and a subject matter expert in modeling, simulation, analysis and condition monitoring, Servitization and Physical Asset Management, third edition, is an invaluable reference to those considering providing asset management services for the products they design and manufacture. It is also meant to support middle management wishing to know what needs to be done to look after the assets they are responsible for and who to approach for help, and academics doing research in this field. Michael Provost, is a British engineer with a doctoral degree in thermal power from Cranfield University.

Time Series for Data Scientists

Author : Juana Sanchez
Publisher : Cambridge University Press
ISBN 13 : 1108837778
Total Pages : 477 pages
Book Rating : 4.1/5 (88 download)

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Book Synopsis Time Series for Data Scientists by : Juana Sanchez

Download or read book Time Series for Data Scientists written by Juana Sanchez and published by Cambridge University Press. This book was released on 2023-04-30 with total page 477 pages. Available in PDF, EPUB and Kindle. Book excerpt: A user-friendly, introductory, learning-by-doing bridge between classical and machine learning time series analysis with R.

Data Mining

Author : Ian H. Witten
Publisher : Morgan Kaufmann
ISBN 13 : 0128043571
Total Pages : 655 pages
Book Rating : 4.1/5 (28 download)

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Book Synopsis Data Mining by : Ian H. Witten

Download or read book Data Mining written by Ian H. Witten and published by Morgan Kaufmann. This book was released on 2016-10-01 with total page 655 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research. Please visit the book companion website at https://www.cs.waikato.ac.nz/~ml/weka/book.html. It contains - Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book - Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book - Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc. - Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects - Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods - Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface - Includes open-access online courses that introduce practical applications of the material in the book