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Ajouter au panierEtat : New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.
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Vendeur : California Books, Miami, FL, Etats-Unis
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Ajouter au panierPaperback. Etat : Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
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Edité par Bpb Publications 3/26/2024, 2024
ISBN 10 : 9355519141 ISBN 13 : 9789355519146
Langue: anglais
Vendeur : BargainBookStores, Grand Rapids, MI, Etats-Unis
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Ajouter au panierPaperback or Softback. Etat : New. Modern Data Mining with Python: A Risk-Managed Approach to Developing and Deploying Explainable and Efficient Algorithms Using Modelops 1.66. Book.
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Ajouter au panierPaperback or Softback. Etat : New. Modern Data Mining Algorithms in C++ and Cuda C: Recent Developments in Feature Extraction and Selection Algorithms for Data Science 0.93. Book.
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ISBN 10 : 1484277252 ISBN 13 : 9781484277256
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Ajouter au panierEtat : Brand New. New.SoftCover International edition. Different ISBN and Cover image but contents are same as US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
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Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
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Ajouter au panierEtat : New. In English.
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
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Vendeur : Revaluation Books, Exeter, Royaume-Uni
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Ajouter au panierPaperback. Etat : Brand New. 237 pages. 10.00x7.00x0.50 inches. In Stock.
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Edité par Apress, Apress Jun 2020, 2020
ISBN 10 : 1484259874 ISBN 13 : 9781484259870
Langue: anglais
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Ajouter au panierTaschenbuch. Etat : Neu. Neuware -Discover a variety of data-mining algorithms that are useful for selecting small sets of important features from among unwieldy masses of candidates, or extracting useful features from measured variables.As a serious data miner you will often be faced with thousands of candidate features for your prediction or classification application, with most of the features being of little or no value. Yoüll know that many of these features may be useful only in combination with certain other features while being practically worthless alone or in combination with most others. Some features may have enormous predictive power, but only within a small, specialized area of the feature space. The problems that plague modern data miners are endless. This book helps you solve this problem by presenting modern feature selection techniques and the code to implement them. Some of these techniques are:Forward selection component analysisLocal feature selectionLinking features and a target with a hidden Markov modelImprovements on traditional stepwise selectionNominal-to-ordinal conversionAll algorithms are intuitively justified and supported by the relevant equations and explanatory material. The author also presents and explains complete, highly commented source code.The example code is in C++ and CUDA C but Python or other code can be substituted; the algorithm is important, not the code that's used to write it.What You Will LearnCombine principal component analysis with forward and backward stepwise selection to identify a compact subset of a large collection of variables that captures the maximum possible variation within the entire set.Identify features that may have predictive power over only a small subset of the feature domain. Such features can be profitably used by modern predictive models but may be missed by other feature selection methods.Find an underlying hidden Markov model that controls the distributions of feature variables and the target simultaneously. The memory inherent in this method is especially valuable in high-noise applications such as prediction of financial markets.Improve traditional stepwise selection in three ways: examine a collection of 'best-so-far' feature sets; test candidate features for inclusion with cross validation to automatically and effectively limit model complexity; and at each step estimate the probability that our results so far could be just the product of random good luck. We also estimate the probability that the improvement obtained by adding a new variable could have been just good luck. Take a potentially valuable nominal variable (a category or class membership) that is unsuitable for input to a prediction model, and assign to each category a sensible numeric value that can be used as a model input.Who This Book Is ForIntermediate to advanced data science programmers and analysts.APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 240 pp. Englisch.
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Vendeur : Lakeside Books, Benton Harbor, MI, Etats-Unis
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Ajouter au panierEtat : New. Brand New! Not Overstocks or Low Quality Book Club Editions! Direct From the Publisher! We're not a giant, faceless warehouse organization! We're a small town bookstore that loves books and loves it's customers! Buy from Lakeside Books!
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Vendeur : Revaluation Books, Exeter, Royaume-Uni
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Ajouter au panierPaperback. Etat : Brand New. 286 pages. 10.00x7.00x1.00 inches. In Stock.
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Vendeur : dsmbooks, Liverpool, Royaume-Uni
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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Ajouter au panierTaschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Data miner's survival kit for explainable, effective, and efficient algorithms enabling responsible decision-making¿DESCRIPTION'Modern Data Mining with Python' is a guidebook for responsibly implementing data mining techniques that involve collecting, storing, and analyzing large amounts of structured and unstructured data to extract useful insights and patterns.Enter into the world of data mining and machine learning. Use insights from various data sources, from social media to credit card transactions. Master statistical tools, explore data trends, and patterns. Understand decision trees and artificial neural networks (ANNs). Manage high-dimensional data with dimensionality reduction. Explore binary classification with logistic regression. Spot concealed patterns with unsupervised learning. Analyze text with recurrent neural networks (RNNs) and visuals with convolutional neural networks (CNNs). Ensure model compliance with regulatory standards.After reading this book, readers will be equipped with the skills and knowledge necessary to use Python for data mining and analysis in an industry set-up. They will be able to analyze and implement algorithms on large structured and unstructured datasets.WHAT YOU WILL LEARN¿ Explore the data mining spectrum ranging from data exploration and statistics.¿ Gain hands-on experience applying modern algorithms to real-world problems in the financial industry.¿ Develop an understanding of various risks associated with model usage in regulated industries.¿ Gain knowledge about best practices and regulatory guidelines to mitigate model usage-related risk in key banking areas.¿ Develop and deploy risk-mitigated algorithms on self-serve ModelOps platforms.WHO THIS BOOK IS FORThis book is for a wide range of early career professionals and students interested in data mining or data science with a financial services industry focus. Senior industry professionals, and educators, trying to implement data mining algorithms can benefit as well.