Practical Machine Learning For Data Analysis Using Python, Subasi, Abdulhamit
Автор: Strang Gilbert Название: Linear Algebra and Learning from Data ISBN: 0692196382 ISBN-13(EAN): 9780692196380 Издательство: Cambridge Academ Рейтинг: Цена: 9978.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.
Автор: Darren Cook Название: Practical Machine Learning with H2O ISBN: 149196460X ISBN-13(EAN): 9781491964606 Издательство: Wiley Рейтинг: Цена: 6334.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms.
Автор: Berrar Daniel P., Dubitzky Werner, Granzow Martin Название: A Practical Approach to Microarray Data Analysis ISBN: 1402072600 ISBN-13(EAN): 9781402072604 Издательство: Springer Рейтинг: Цена: 4890.00 р. 6986.00-30% Наличие на складе: Есть (1 шт.) Описание: A Practical Approach to Microarray Data Analysis is for all life scientists, statisticians, computer experts, technology developers, managers, and other professionals tasked with developing, deploying, and using microarray technology including the necessary computational infrastructure and analytical tools. The book addresses the requirement of scientists and researchers to gain a basic understanding of microarray analysis methodologies and tools. It is intended for students, teachers, researchers, and research managers who want to understand the state of the art and of the presented methodologies and the areas in which gaps in our knowledge demand further research and development. The book is designed to be used by the practicing professional tasked with the design and analysis of microarray experiments or as a text for a senior undergraduate- or graduate level course in analytical genetics, biology, bioinformatics, computational biology, statistics and data mining, or applied computer science. Key topics covered include: -Format of result from data analysis, analytical modeling/experimentation; -Validation of analytical results; -Data analysis/Modeling task; -Analysis/modeling tools; -Scientific questions, goals, and tasks; -Application; -Data analysis methods; -Criteria for assessing analysis methodologies, models, and tools.
Описание: Like the popular second edition, Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining?including both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. <br><br>Complementing the book is a fully functional platform-independent open source Weka software for machine learning, available for free download. <br><br>The book is a major revision of the second edition that appeared in 2005. While the basic core remains the same, it has been updated to reflect the changes that have taken place over the last four or five years. The highlights for the updated new edition include completely revised technique sections; new chapter on Data Transformations, new chapter on Ensemble Learning, new chapter on Massive Data Sets, a new ?book release? version of the popular Weka machine learning open source software (developed by the authors and specific to the Third Edition); new material on ?multi-instance learning?; new information on ranking the classification, plus comprehensive updates and modernization throughout. All in all, approximately 100 pages of new material.<br> <br><br>* Thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques<br><br>* Algorithmic methods at the heart of successful data mining?including tired and true methods as well as leading edge methods<br><br>* Performance improvement techniques that work by transforming the input or output<br><br>* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization?in an updated, interactive interface. <br>
Автор: Gollapudi Sunila Название: Practical Machine Learning ISBN: 178439968X ISBN-13(EAN): 9781784399689 Издательство: Неизвестно Рейтинг: Цена: 9378.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Learn how to build Machine Learning applications to solve real-world data analysis challenges with this Machine Learning book - packed with practical tutorials
Key Features
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
Book Description
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 Na ve 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.
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
Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach presents how machine learning and biomedical signal processing methods can be used in biomedical signal analysis. Different machine learning applications in biomedical signal analysis, including those for electrocardiogram, electroencephalogram and electromyogram are described in a practical and comprehensive way, helping readers with limited knowledge. Sections cover biomedical signals and machine learning techniques, biomedical signals, such as electroencephalogram (EEG), electromyogram (EMG) and electrocardiogram (ECG), different signal-processing techniques, signal de-noising, feature extraction and dimension reduction techniques, such as PCA, ICA, KPCA, MSPCA, entropy measures, and other statistical measures, and more.
This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need a cogent resource on the most recent and promising machine learning techniques for biomedical signals analysis.
Provides comprehensive knowledge in the application of machine learning tools in biomedical signal analysis for medical diagnostics, brain computer interface and man/machine interaction
Explains how to apply machine learning techniques to EEG, ECG and EMG signals
Gives basic knowledge on predictive modeling in biomedical time series and advanced knowledge in machine learning for biomedical time series
Описание: This step-by-step guide teaches you how to build practical deep learning applications for the cloud and mobile using a hands-on approach.
Автор: Simovici, Dan A (univ Of Massachusetts At Boston, Usa) Название: Mathematical analysis for machine learning and data mining ISBN: 9813229683 ISBN-13(EAN): 9789813229686 Издательство: World Scientific Publishing Рейтинг: Цена: 51480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This compendium provides a self-contained introduction to mathematical analysis in the field of machine learning and data mining. The mathematical analysis component of the typical mathematical curriculum for computer science students omits these very important ideas and techniques which are indispensable for approaching specialized area of machine learning centered around optimization such as support vector machines, neural networks, various types of regression, feature selection, and clustering. The book is of special interest to researchers and graduate students who will benefit from these application areas discussed in the book. Related Link(s)
Автор: Amaral Turkman Maria Antуnia Название: Institute of Mathematical Statistics Textbooks ISBN: 1108703747 ISBN-13(EAN): 9781108703741 Издательство: Cambridge Academ Рейтинг: Цена: 6019.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book explains the fundamental ideas of Bayesian analysis, with a focus on computational methods such as MCMC and available software such as R/R-INLA, OpenBUGS, JAGS, Stan, and BayesX. It is suitable as a textbook for a first graduate-level course and as a user`s guide for researchers and graduate students from beyond statistics.
Автор: Wainwright Martin J Название: Cambridge Series in Statistical and Probabilistic Mathematic ISBN: 1108498027 ISBN-13(EAN): 9781108498029 Издательство: Cambridge Academ Рейтинг: Цена: 10771.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Recent years have seen an explosion in the volume and variety of data collected in scientific disciplines from astronomy to genetics and industrial settings ranging from Amazon to Uber. This graduate text equips readers in statistics, machine learning, and related fields to understand, apply, and adapt modern methods suited to large-scale data.
Автор: Bouveyron, Charles Celeux, Gilles Murphy, T. Brendan (university College Dublin) Raftery, Adrian E. (university Of Washington) Название: Cambridge series in statistical and probabilistic mathematics ISBN: 110849420X ISBN-13(EAN): 9781108494205 Издательство: Cambridge Academ Рейтинг: Цена: 11563.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This accessible but rigorous introduction is written for advanced undergraduates and beginning graduate students in data science, as well as researchers and practitioners. It shows how a statistical framework yields sound estimation, testing and prediction methods, using extensive data examples and providing R code for many methods.
Название: Machine Learning Applications Using Python ISBN: 1484237862 ISBN-13(EAN): 9781484237861 Издательство: Springer Рейтинг: Цена: 10480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Part 1: HealthcareChapter 1. Overview of machine learning in healthcare.Chapter 2. Key technological advancements in healthcare.Chapter 3. How to implement machine learning in healthcare.Chapter 4. Case studies on how organizations are changing the game in the market.Chapter 5. Pitfalls to avoid while implementing machine learning in healthcare.Chapter 6. Healthcare specific innovative Ideas for monetizing machine learning. Part 2: Retail Chapter 7. Overview of machine learning in Retail.Chapter 8. Key technological advancements in Retail.Chapter 9. How to implement machine learning in Retail.Chapter 10. Case studies on how organizations are changing the game in the market. c. One discussion based case study. d. One practical case study with Python code.Chapter 11. Pitfalls to avoid while implementing machine learning in retail.Chapter 12. Retail specific innovative Ideas for monetizing machine learning. Part 3: Finance Chapter 13. Overview of machine learning in Finance.Chapter 14. Key technological advancements in Finance.Chapter 15. How to implement machine learning in Finance.Chapter 16. Case studies on how organizations are changing the game in the market. e. One discussion based case study. f. One practical case study with Python code.Chapter 17. Pitfalls to avoid while implementing machine learning in Finance.Chapter 18. Finance specific innovative Ideas for monetizing machine learning.
ООО "Логосфера " Тел:+7(495) 980-12-10 www.logobook.ru
