Practical Machine Learning Illustrated With Knime
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Practical Machine Learning Illustrated With Knime
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Author : Yu Geng
language : en
Publisher: Springer Nature
Release Date : 2024-08-29
Practical Machine Learning Illustrated With Knime written by Yu Geng and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-29 with Mathematics categories.
This book guides professionals and students from various backgrounds to use machine learning in their own fields with low-code platform KNIME and without coding. Many people from various industries need use machine learning to solve problems in their own domains. However, machine learning is often viewed as the domain of programmers, especially for those who are familiar with Python. It is too hard for people from different backgrounds to learn Python to use machine learning. KNIME, the low-code platform, comes to help. KNIME helps people use machine learning in an intuitive environment, enabling everyone to focus on what to do instead of how to do. This book helps the readers gain an intuitive understanding of the basic concepts of machine learning through illustrations to practice machine learning in their respective fields. The author provides a practical guide on how to participate in Kaggle completions with KNIME to practice machine learning techniques.
Machine Learning With Knime Analytics Platform
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Author : Nickholas Anting
language : en
Publisher:
Release Date : 2020
Machine Learning With Knime Analytics Platform written by Nickholas Anting and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Data mining categories.
"Machine Learning with KNIME Analytics Platform is a book written for those who want to learn about fundamental of machine learning and how to create a machine learning workflow using KNIME Analytics Platform. The book will teach user how to create a machine learning workflow, step-by-step; from data preparation until model validation and deployment in predictive analytics task. NO CODING requires. Only drag-and-drop mechanism with intuitive functioning nodes. Design for professional individual in various industries who work with data. KNIME Analytics Platform is a visual programming tools that allow user to process data and build their machine learning algorithm with drag-and-drop mechanism of the functioning node. It is an open-source and free software package for data science with full function. NO CODING needed."--Back cover
Practical Machine Learning
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Author : Sunila Gollapudi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2016-01-30
Practical Machine Learning written by Sunila Gollapudi and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-01-30 with Computers categories.
Tackle the real-world complexities of modern machine learning with innovative, cutting-edge, techniques About This Book Fully-coded working examples using a wide range of machine learning libraries and tools, including Python, R, Julia, and Spark Comprehensive practical solutions taking you into the future of machine learning Go a step further and integrate your machine learning projects with Hadoop Who This Book Is For This book has been created for data scientists who want to see machine learning in action and explore its real-world application. With guidance on everything from the fundamentals of machine learning and predictive analytics to the latest innovations set to lead the big data revolution into the future, this is an unmissable resource for anyone dedicated to tackling current big data challenges. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately. What You Will Learn Implement a wide range of algorithms and techniques for tackling complex data Get to grips with some of the most powerful languages in data science, including R, Python, and Julia Harness the capabilities of Spark and Hadoop to manage and process data successfully Apply the appropriate machine learning technique to address real-world problems Get acquainted with Deep learning and find out how neural networks are being used at the cutting-edge of machine learning Explore the future of machine learning and dive deeper into polyglot persistence, semantic data, and more In Detail Finding meaning in increasingly larger and more complex datasets is a growing demand of the modern world. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. Machine learning uses complex algorithms to make improved predictions of outcomes based on historical patterns and the behaviour of data sets. Machine learning can deliver dynamic insights into trends, patterns, and relationships within data, immensely valuable to business growth and development. This book explores an extensive range of machine learning techniques uncovering hidden tricks and tips for several types of data using practical and real-world examples. While machine learning can be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles. Inside, a full exploration of the various algorithms gives you high-quality guidance so you can begin to see just how effective machine learning is at tackling contemporary challenges of big data. This is the only book you need to implement a whole suite of open source tools, frameworks, and languages in machine learning. We will cover the leading data science languages, Python and R, and the underrated but powerful Julia, as well as a range of other big data platforms including Spark, Hadoop, and Mahout. Practical Machine Learning is an essential resource for the modern data scientists who want to get to grips with its real-world application. With this book, you will not only learn the fundamentals of machine learning but dive deep into the complexities of real world data before moving on to using Hadoop and its wider ecosystem of tools to process and manage your structured and unstructured data. You will explore different machine learning techniques for both supervised and unsupervised learning; from decision trees to Naive Bayes classifiers and linear and clustering methods, you will learn strategies for a truly advanced approach to the statistical analysis of data. The book also explores the cutting-edge advancements in machine learning, with worked examples and guidance on deep learning and reinforcement learning, providing you with practical demonstrations and samples that help take the theory–and mystery–out of even the most advanced machine learning methodologies. Style and approach A practical data science tutorial designed to give you an insight into the practical application of machine learning, this book takes you through complex concepts and tasks in an accessible way. Featuring information on a wide range of data science techniques, Practical Machine Learning is a comprehensive data science resource.
Introduction To Machine Learning With Knime
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Author :
language : en
Publisher:
Release Date : 2019
Introduction To Machine Learning With Knime written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Learn KNIME, a popular open-source platform for predictive analytics and machine learning. Discover how to use KNIME for merging and aggregation, modeling, data scoring, and more.
Practical Machine Learning
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Author : Andrew H. Johnston
language : en
Publisher: No Starch Press
Release Date : 2018-07-17
Practical Machine Learning written by Andrew H. Johnston and has been published by No Starch Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-17 with Machine learning categories.
An accessible guide to machine learning principles for programmers. Features hands-on example projects, real-world case studies, and easy-to-understand explanations. Practical Machine Learning is a clear, hands-on introduction to machine learning written for programmers -- no extensive background in math required. You'll learn the fundamentals of machine learning and how to use WEKA, a suite of free, open-source tools to build and test "smart" algorithms and incorporate them into your code. The book breaks down the machine learning process, including conducting litmus tests to develop a strategy, preparing your data, preprocessing, and increasing the performance of your algorithm through data normalization. You'll test your new skills with three hands-on experiments: running algorithms that rank customer applications, determine whether a website is malicious, and suggest recommended products. Rather than wallowing in theory, the book is packed with real-world examples, code snippets, and case-studies that put each lesson into practice. Wrapping up with an overview of how to identify Big Data and manage extremely large datasets, Practical Machine Learning is an accessible introduction to this rapidly growing industry, perfect for any programmer looking to apply its principles to their work.
Practical Machine Learning
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Author : Ally S. Nyamawe
language : en
Publisher: CRC Press
Release Date : 2025-02-07
Practical Machine Learning written by Ally S. Nyamawe and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-07 with Computers categories.
The book provides an accessible, comprehensive introduction for beginners to machine learning, equipping them with the fundamental skills and techniques essential for this field. It enables beginners to construct practical, real-world solutions powered by machine learning across diverse application domains. It demonstrates the fundamental techniques involved in data collection, integration, cleansing, transformation, development, and deployment of machine learning models. This book emphasizes the importance of integrating responsible and explainable AI into machine learning models, ensuring these principles are prioritized rather than treated as an afterthought. To support learning, this book also offers information on accessing additional machine learning resources such as datasets, libraries, pre-trained models, and tools for tracking machine learning models. This is a core resource for students and instructors of machine learning and data science looking for a beginner-friendly material which offers real-world applications and takes ethical discussions into account. The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license.
Machine Learning Explained A Practical Guide To Data Driven Decision Making
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Author : Abdelhamid ZAIDI
language : en
Publisher: Xoffencerpublication
Release Date : 2023-10-30
Machine Learning Explained A Practical Guide To Data Driven Decision Making written by Abdelhamid ZAIDI and has been published by Xoffencerpublication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-30 with Computers categories.
During the course of the process of making a choice, we rely on a variety of presumptions, premises, and the circumstances; all of this is directed by the goal that is related with the decision itself. However, the premises and the knowledge of the corporation are dependent on our data since they are an essential component of our organization as a system. The context and the assumptions are both external factors that are beyond the control of any decision maker. Both the background and the assumptions represent outside forces that are not within the control of any decision maker. A prominent example of a conceptual error is the misunderstanding that exists between data and information, which in reality correspond to entirely distinct ideas. This misunderstanding is a common occurrence. In point of fact, information and data cannot in any way be substituted for one another in any context. To put this another way, there is no guarantee that the data will be consistent, comparable, or traceable, despite the fact that we are able to collect data from a broad variety of distinct data sources. This is because there are so many diverse data sources. Because of this, in order for us to make a decision, we need to have a good comprehension of both the component that is presently being examined and the data that is linked with it at the present time. Only then will we be able to make an informed choice. The identification of the system itself is the first step that must be taken before any other aspects of the system, such as its boundaries, context, subsystems, feedback, inputs, and outputs, can be determined. Because of this, it is significant because, according to the point of view connected with general system theory, it is necessary to identify the system that is being discussed. In order to get a more in-depth understanding of the system, we must first begin by defining it. After that, we may proceed to quantifying each associated quality in order to achieve this goal. This would make it possible for us to have a better understanding of the system. Because of this, in order for us to collect information on the topic of the research, we will initially need to measure it in order to quantify the characteristics that are associated with it. For this, we will need to perform certain measurements on the subject. After that, we will establish the indicators that will be applied for the purpose of determining the value of each measure, and we will do so by utilizing the results of the stage that came before it. Within the context of this method, the Measurement and Evaluation (M&E) process can gain an advantage from making use of a conceptual framework that is built on top of an underlying ontology. The M&E framework makes it possible to describe the basic ideas, which prepares the way for a measurement process to be carried out in a manner that is consistent and repeatable. This is made possible by the fact that the framework makes it possible to specify the essential concepts. The ability of a measuring process to be automated is of the utmost significance, even if it is required for a measuring process to give findings that are consistent, comparable, and traceable. The ability of a measuring process to be automated is of the utmost relevance. Because the activities that take place in today's economy take place in real time, we need to pay considerable attention to the use of online monitoring in order to notice and avoid a variety of different scenarios while they are happening. Because of this, we will be able to reduce risk while maximizing our efficiency. In this regard, the functionality of the measurement and evaluation frameworks is an extremely valuable asset, as they make it possible to organize and automate the process of measuring in a manner that is consistent. This makes the frameworks an exceptionally helpful asset. As a result of this, the frameworks are a very useful asset. As soon as it is feasible to guarantee that the measurements are comparable, consistent, and traceable, the method of decision-making will naturally be based on their history, which will consist of the measurements collected throughout the years. This will be the case as soon as it is possible to guarantee that the measurements are comparable, consistent, and traceable. This will take place as soon as it is practical to assure that the measurements are comparable, consistent, and traceable. In this regard, the organizational memory is of special importance due to the fact that it makes it possible to store prior organizational experience and knowledge in order to get ready for future proposals (that is, as the foundation for a range of different assumptions and premises, among other things). In this regard, the organizational memory is of particular use. Because of this, the organizational memory is a component that is of very high importance. Measurements and the experiences that are associated with them provide continuous nourishment for the organizational memory, and the organizational memory provides the foundation for the feedback that is utilized in the process of decision making.
Machine Learning
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Author : Ryan Turner
language : en
Publisher: Publishing Factory
Release Date : 2020-04-19
Machine Learning written by Ryan Turner and has been published by Publishing Factory this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-19 with Computers categories.
Are you someone who is interested in how the next generation of machines can help you? Is Artificial Intelligence something to be feared, or do you imagine it that it will change our lives for the better? This book will provide the answers you need. Life is becoming ever more complex as we struggle to keep up with technology and use it to our best advantage. It is also more hectic and less certain, even in some of the mundane aspects of our lives, so that we are constantly trying to keep pace. New advancements in technology are paving the way to making life easier for billions and now things like Machine Learning and AI are changing the way we live. In this book, Machine Learning: The Ultimate Beginner’s Guide to Learn Machine Learning, Artificial Intelligence & Neural Networks Step by Step, you will see how this new technology continuously improves itself, can identify trends and patterns with ease and handles a wide variety of data, with chapters that explore: • Teaching the basic principles of Machine Learning • Why it is important and the many benefits that it provides • How Machine Learning differs from conventional programming • The fundamentals of algorithms • Challenges with Machine Learning and how you can easily overcome them • How it is going to change the future and make life easier • And much more… Machine Learning and AI are more than just science fiction. They are here now and undoubtedly will remain, improving and enhancing our lives in many ways, from the everyday to the vitally important. This book provides a platform that will give you a comprehensive understanding, that is second to none, of machine learning and its place in the world today. Get a copy now and see how Machine Learning will change your life!
Knime Workflow Design And Automation
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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-16
Knime Workflow Design And Automation written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-16 with Computers categories.
"KNIME Workflow Design and Automation" Unlock the full potential of KNIME with "KNIME Workflow Design and Automation," a comprehensive guide for data professionals, engineers, and enterprise architects seeking to master advanced workflow engineering and automation. This book delves deeply into the architecture and inner workings of the KNIME platform, presenting robust best practices for workflow initialization, structuring projects for maintainability, and integrating with source control systems. From granular explanations of node architecture and execution semantics to sophisticated data modeling and metadata handling, each chapter equips readers with the tools and understanding necessary to design efficient, scalable, and reproducible analytics workflows. Building on a solid foundation, the book explores the complexities of large-scale data wrangling, encompassing high-performance batch and real-time processing, diverse data source integration, and advanced cleaning and transformation techniques. Readers will discover comprehensive methods for orchestrating and automating workflows, including dynamic control flows, reusable components, robust error handling, and real-time monitoring. Detailed insights into interoperability—covering databases, APIs, cloud storage, and streaming systems—enable seamless enterprise data integration with a strong emphasis on security and governance. Moving beyond the fundamentals, the book presents cutting-edge material on machine learning workflow patterns, automated feature engineering, model validation, and scalable deployment strategies. Advanced topics such as custom node development, scripting integrations, and enterprise-scale workflow governance ensure that professionals can extend KNIME's capabilities to fit any strategic application. Illustrated with real-world case studies from life sciences, finance, manufacturing, marketing, and MLOps, "KNIME Workflow Design and Automation" serves as an authoritative reference and practical roadmap for delivering high-impact data solutions across industries.
Fundamentals And Methods Of Machine And Deep Learning
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Author : Pradeep Singh
language : en
Publisher: John Wiley & Sons
Release Date : 2022-03-02
Fundamentals And Methods Of Machine And Deep Learning written by Pradeep Singh and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-02 with Computers categories.
FUNDAMENTALS AND METHODS OF MACHINE AND DEEP LEARNING The book provides a practical approach by explaining the concepts of machine learning and deep learning algorithms, evaluation of methodology advances, and algorithm demonstrations with applications. Over the past two decades, the field of machine learning and its subfield deep learning have played a main role in software applications development. Also, in recent research studies, they are regarded as one of the disruptive technologies that will transform our future life, business, and the global economy. The recent explosion of digital data in a wide variety of domains, including science, engineering, Internet of Things, biomedical, healthcare, and many business sectors, has declared the era of big data, which cannot be analysed by classical statistics but by the more modern, robust machine learning and deep learning techniques. Since machine learning learns from data rather than by programming hard-coded decision rules, an attempt is being made to use machine learning to make computers that are able to solve problems like human experts in the field. The goal of this book is to present a??practical approach by explaining the concepts of machine learning and deep learning algorithms with applications. Supervised machine learning algorithms, ensemble machine learning algorithms, feature selection, deep learning techniques, and their applications are discussed. Also included in the eighteen chapters is unique information which provides a clear understanding of concepts by using algorithms and case studies illustrated with applications of machine learning and deep learning in different domains, including disease prediction, software defect prediction, online television analysis, medical image processing, etc. Each of the chapters briefly described below provides both a chosen approach and its implementation. Audience Researchers and engineers in artificial intelligence, computer scientists as well as software developers.