Refined Mechanism For Data Cleaning And Machine Learning
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Refined Mechanism For Data Cleaning And Machine Learning
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Author : 余艾玨
language : en
Publisher:
Release Date : 2018
Refined Mechanism For Data Cleaning And Machine Learning written by 余艾玨 and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
Machine Learning Refined
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Author : Jeremy Watt
language : en
Publisher: Cambridge University Press
Release Date : 2020-01-09
Machine Learning Refined written by Jeremy Watt and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-09 with Computers categories.
An intuitive approach to machine learning covering key concepts, real-world applications, and practical Python coding exercises.
Data Cleaning The Ultimate Practical Guide
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Author : Lee Baker
language : en
Publisher: Lee Baker
Release Date : 2022-11-07
Data Cleaning The Ultimate Practical Guide written by Lee Baker and has been published by Lee Baker this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-07 with Business & Economics categories.
Data visualisation is sexy. So are Bayesian Belief Nets and Artificial Neural Networks. You can’t get to do any of these things, though, if your data are dirty. Your analysis package will just stare back at you, saying ‘computer says no’. But just how do you get the clean data that these packages need? What is ‘clean data’? And, for that matter, what is ‘dirty data’? Data Cleaning: The Ultimate Practical Guide is a guide to understanding what dirty data is, and how it gets into your dataset. More than that, it is a guide to helping you prevent most types of dirty data getting into your dataset in the first place, and cleaning out quickly and efficiently the remaining errors, so you can have clean, fit-for-purpose and analysis-ready data. So that your data are ready to change the world! Data Cleaning: The Ultimate Practical Guide is a snappy little non-threatening book about everything you ever wanted to know (but were afraid to ask) about the craft of cleaning and preparing your data for the sexier parts of your analysis. First, I’ll explain about the 4 phases of data cleaning. Then I’ll show you the 6 different types of dirty data that tend to find a way into your dataset. You’ll learn about the 5 data collection methods typically used in research, and you’ll get a 5 step method of cleaning data. Finally, you’ll learn about the 4 data pre-processing steps using summary statistics that will help you get your data fit-for-purpose and analysis-ready. Best of all, there is no technical jargon – it is written in plain English and is perfect for beginners! By the time you’ve read this short book, you’ll know more about data collection and cleaning than most people around you! Discover how to clean your data quickly and effectively. Get this book, TODAY!
Data Cleaning
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Author : Xu Chu
language : en
Publisher:
Release Date : 2019
Data Cleaning written by Xu Chu 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.
Machine Learning Infrastructure And Best Practices For Software Engineers
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Author : Miroslaw Staron
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-01-31
Machine Learning Infrastructure And Best Practices For Software Engineers written by Miroslaw Staron 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 2024-01-31 with Computers categories.
Efficiently transform your initial designs into big systems by learning the foundations of infrastructure, algorithms, and ethical considerations for modern software products Key Features Learn how to scale-up your machine learning software to a professional level Secure the quality of your machine learning pipeline at runtime Apply your knowledge to natural languages, programming languages, and images Book DescriptionAlthough creating a machine learning pipeline or developing a working prototype of a software system from that pipeline is easy and straightforward nowadays, the journey toward a professional software system is still extensive. This book will help you get to grips with various best practices and recipes that will help software engineers transform prototype pipelines into complete software products. The book begins by introducing the main concepts of professional software systems that leverage machine learning at their core. As you progress, you’ll explore the differences between traditional, non-ML software, and machine learning software. The initial best practices will guide you in determining the type of software you need for your product. Subsequently, you will delve into algorithms, covering their selection, development, and testing before exploring the intricacies of the infrastructure for machine learning systems by defining best practices for identifying the right data source and ensuring its quality. Towards the end, you’ll address the most challenging aspect of large-scale machine learning systems – ethics. By exploring and defining best practices for assessing ethical risks and strategies for mitigation, you will conclude the book where it all began – large-scale machine learning software.What you will learn Identify what the machine learning software best suits your needs Work with scalable machine learning pipelines Scale up pipelines from prototypes to fully fledged software Choose suitable data sources and processing methods for your product Differentiate raw data from complex processing, noting their advantages Track and mitigate important ethical risks in machine learning software Work with testing and validation for machine learning systems Who this book is for If you’re a machine learning engineer, this book will help you design more robust software, and understand which scaling-up challenges you need to address and why. Software engineers will benefit from best practices that will make your products robust, reliable, and innovative. Decision makers will also find lots of useful information in this book, including guidance on what to look for in a well-designed machine learning software product.
Machine Learning And Data Mining For Computer Security
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Author : Marcus A. Maloof
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-02-27
Machine Learning And Data Mining For Computer Security written by Marcus A. Maloof and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-02-27 with Computers categories.
"Machine Learning and Data Mining for Computer Security" provides an overview of the current state of research in machine learning and data mining as it applies to problems in computer security. This book has a strong focus on information processing and combines and extends results from computer security. The first part of the book surveys the data sources, the learning and mining methods, evaluation methodologies, and past work relevant for computer security. The second part of the book consists of articles written by the top researchers working in this area. These articles deals with topics of host-based intrusion detection through the analysis of audit trails, of command sequences and of system calls as well as network intrusion detection through the analysis of TCP packets and the detection of malicious executables. This book fills the great need for a book that collects and frames work on developing and applying methods from machine learning and data mining to problems in computer security.
Cybersecurity Data Science
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Author : Scott Mongeau
language : en
Publisher: Springer Nature
Release Date : 2021-10-01
Cybersecurity Data Science written by Scott Mongeau and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-01 with Computers categories.
This book encompasses a systematic exploration of Cybersecurity Data Science (CSDS) as an emerging profession, focusing on current versus idealized practice. This book also analyzes challenges facing the emerging CSDS profession, diagnoses key gaps, and prescribes treatments to facilitate advancement. Grounded in the management of information systems (MIS) discipline, insights derive from literature analysis and interviews with 50 global CSDS practitioners. CSDS as a diagnostic process grounded in the scientific method is emphasized throughout Cybersecurity Data Science (CSDS) is a rapidly evolving discipline which applies data science methods to cybersecurity challenges. CSDS reflects the rising interest in applying data-focused statistical, analytical, and machine learning-driven methods to address growing security gaps. This book offers a systematic assessment of the developing domain. Advocacy is provided to strengthen professional rigor and best practices in the emerging CSDS profession. This book will be of interest to a range of professionals associated with cybersecurity and data science, spanning practitioner, commercial, public sector, and academic domains. Best practices framed will be of interest to CSDS practitioners, security professionals, risk management stewards, and institutional stakeholders. Organizational and industry perspectives will be of interest to cybersecurity analysts, managers, planners, strategists, and regulators. Research professionals and academics are presented with a systematic analysis of the CSDS field, including an overview of the state of the art, a structured evaluation of key challenges, recommended best practices, and an extensive bibliography.
Physics Of Data Science And Machine Learning
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Author : Ijaz A. Rauf
language : en
Publisher: CRC Press
Release Date : 2021-11-28
Physics Of Data Science And Machine Learning written by Ijaz A. Rauf and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-28 with Computers categories.
Physics of Data Science and Machine Learning links fundamental concepts of physics to data science, machine learning, and artificial intelligence for physicists looking to integrate these techniques into their work. This book is written explicitly for physicists, marrying quantum and statistical mechanics with modern data mining, data science, and machine learning. It also explains how to integrate these techniques into the design of experiments, while exploring neural networks and machine learning, building on fundamental concepts of statistical and quantum mechanics. This book is a self-learning tool for physicists looking to learn how to utilize data science and machine learning in their research. It will also be of interest to computer scientists and applied mathematicians, alongside graduate students looking to understand the basic concepts and foundations of data science, machine learning, and artificial intelligence. Although specifically written for physicists, it will also help provide non-physicists with an opportunity to understand the fundamental concepts from a physics perspective to aid in the development of new and innovative machine learning and artificial intelligence tools. Key Features: Introduces the design of experiments and digital twin concepts in simple lay terms for physicists to understand, adopt, and adapt. Free from endless derivations; instead, equations are presented and it is explained strategically why it is imperative to use them and how they will help in the task at hand. Illustrations and simple explanations help readers visualize and absorb the difficult-to-understand concepts. Ijaz A. Rauf is an adjunct professor at the School of Graduate Studies, York University, Toronto, Canada. He is also an associate researcher at Ryerson University, Toronto, Canada and president of the Eminent-Tech Corporation, Bradford, ON, Canada.
Icemme 2023
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Author : Nikolaos Freris
language : en
Publisher: European Alliance for Innovation
Release Date : 2024-02-27
Icemme 2023 written by Nikolaos Freris and has been published by European Alliance for Innovation this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-27 with Business & Economics categories.
The 2023 5th International Conference on Economic Management and Model Engineering (ICEMME 2023) was held on November 17-19, 2023 in Beijing, China. The primary objective of this conference is to facilitate the exchange of ideas and knowledge among researchers, scholars, and practitioners in the field of economic management and modeling engineering. Through presentations, discussions, and networking opportunities, participants will have the chance to explore the latest advancements, methodologies, and best practices in these areas. The conference was focused on three main themes: Enterprise Economic Management and Market Mechanism Assessment; Data Statistical Analysis and Economic Forecasting; Industrial Structure Optimization and Economic Green Development. For readers, this collection of papers offers a comprehensive insight into cutting-edge research and case studies, providing valuable information on current trends, challenges, and opportunities in economic management and modeling engineering. Readers will benefit from the diverse perspectives and innovative approaches presented in these papers, inspiring new ideas and solutions for their own research endeavors. Moreover, the positive influence of this conference extends beyond the current discussions. It is expected that the findings and recommendations shared in these proceedings will serve as a foundation for future research in the field of economic management and modeling engineering. By fostering collaboration, knowledge sharing, and academic discourse, this conference aims to contribute to the advancement of the field and stimulate further research initiatives in the years to come.
The Human Element Of Big Data
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Author : Geetam S. Tomar
language : en
Publisher: CRC Press
Release Date : 2016-10-26
The Human Element Of Big Data written by Geetam S. Tomar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-26 with Business & Economics categories.
The proposed book talks about the participation of human in Big Data.How human as a component of system can help in making the decision process easier and vibrant.It studies the basic build structure for big data and also includes advanced research topics.In the field of Biological sciences, it comprises genomic and proteomic data also. The book swaps traditional data management techniques with more robust and vibrant methodologies that focus on current requirement and demand through human computer interfacing in order to cope up with present business demand. Overall, the book is divided in to five parts where each part contains 4-5 chapters on versatile domain with human side of Big Data.