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Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
Editore: O'Reilly Media (edition 1), 2018
ISBN 10: 1491953241 ISBN 13: 9781491953242
Lingua: Inglese
Da: BooksRun, Philadelphia, PA, U.S.A.
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Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
Da: SecondSale, Montgomery, IL, U.S.A.
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Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
Da: Seattle Goodwill, Seattle, WA, U.S.A.
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Aggiungi al carrelloCondizione: Good. May have some shelf-wear due to normal use. Your purchase funds free job training and education in the greater Seattle area. Thank you for supporting Goodwills nonprofit mission!
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Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
Da: Follow Books, SOUTHFIELD, MI, U.S.A.
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Feature Engineering for Machine Learning : Principles and Techniques for Data Scientists
Da: GreatBookPrices, Columbia, MD, U.S.A.
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Feature Engineering for Machine Learning
Da: PBShop.store US, Wood Dale, IL, U.S.A.
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Feature Engineering for Machine Learning
Da: PBShop.store UK, Fairford, GLOS, Regno Unito
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Feature Engineering for Machine Learning : Principles and Techniques for Data Scientists
Da: GreatBookPrices, Columbia, MD, U.S.A.
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Feature Engineering for Machine Learning (Paperback)
Editore: O'Reilly Media, Sebastopol, 2018
ISBN 10: 1491953241 ISBN 13: 9781491953242
Lingua: Inglese
Da: Grand Eagle Retail, Fairfield, OH, U.S.A.
Valutazione del venditore 5 su 5 stelle
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, youundefinedll learn techniques for extracting and transforming featuresundefinedthe numeric representations of raw dataundefinedinto formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering.Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples.Youundefinedll examine:Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transformsNatural text techniques: bag-of-words, n-grams, and phrase detectionFrequency-based filtering and feature scaling for eliminating uninformative featuresEncoding techniques of categorical variables, including feature hashing and bin-countingModel-based feature engineering with principal component analysisThe concept of model stacking, using k-means as a featurization techniqueImage feature extraction with manual and deep-learning techniquesAbout the AuthorAlice is a technical leader in the field of Machine Learning. Her experience spans algorithm and platform development and applications. Currently, she is a Senior Manager in Amazon's Ad Platform. Previous roles include Director of Data Science at GraphLab/Dato/Turi, machine learning researcher at Microsoft Research, Redmond, and postdoctoral fellow at Carnegie Mellon University. She received a Ph.D. in Electrical Engineering and Computer science, and B.A. degrees in Computer Science in Mathematics, all from U.C. Berkeley. Principal Product Manager + Data Scientist for Concur Labs at SAP Concur, designing prototypes, interfaces and future tech for travel and expense. Amanda experiments with projects and programs to make machine learning more accessible. Her side projects include volunteering with the NASA Datanauts and getting outside as much as possible. Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, youll learn techniques for extracting and transforming featuresthe numeric representations of raw datainto formats for machine-learning models. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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EUR 44,93
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EUR 44,93
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Feature Engineering for Machine Learning : Principles and Techniques for Data Scientists
Da: GreatBookPricesUK, Woodford Green, Regno Unito
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EUR 43,92
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Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
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EUR 60,90
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Feature Engineering for Machine Learning
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
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EUR 51,25
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Aggiungi al carrelloPaperback / softback. Condizione: New. New copy - Usually dispatched within 4 working days. 490.
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Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
Da: Ria Christie Collections, Uxbridge, Regno Unito
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Feature Engineering for Machine Learning
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
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Aggiungi al carrelloCondizione: New. 2018. Paperback. Feature engineering is essential to applied machine learning, but using domain knowledge to strengthen your predictive models can be difficult and expensive. To help fill the information gap this complete guide teaches beginning-to-intermediate data scientists how to work with this widely practiced but little discussed topic. Num Pages: 200 pages. BIC Classification: UY. Category: (P) Professional & Vocational. Dimension: 233 x 178 x 15. Weight in Grams: 666. . . . . .
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Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
Da: California Books, Miami, FL, U.S.A.
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Feature Engineering for Machine Learning : Principles and Techniques for Data Scientists
Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Feature Engineering for Machine Learning
Da: Kennys Bookstore, Olney, MD, U.S.A.
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EUR 66,92
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Aggiungi al carrelloCondizione: New. 2018. Paperback. Feature engineering is essential to applied machine learning, but using domain knowledge to strengthen your predictive models can be difficult and expensive. To help fill the information gap this complete guide teaches beginning-to-intermediate data scientists how to work with this widely practiced but little discussed topic. Num Pages: 200 pages. BIC Classification: UY. Category: (P) Professional & Vocational. Dimension: 233 x 178 x 15. Weight in Grams: 666. . . . . . Books ship from the US and Ireland.
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Feature Engineering for Machine Learning Models: Principles and Techniques for Data Scientists
Da: Majestic Books, Hounslow, Regno Unito
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EUR 70,64
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Feature Engineering for Machine Learning Models: Principles and Techniques for Data Scientists
Da: Books Puddle, New York, NY, U.S.A.
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Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
Da: Russell Books, Victoria, BC, Canada
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Prima edizione
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EUR 67,03
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Neuware - 'Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, youll learn techniques for extracting and transforming features-the numeric representations of raw data-into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering.'--Page 4 of cover.
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Feature Engineering for Machine Learning Models: Principles and Techniques for Data Scientists
Da: Revaluation Books, Exeter, Regno Unito
Valutazione del venditore 5 su 5 stelle
EUR 89,97
Convertire valutaEUR 11,87 per la spedizione da Regno Unito a U.S.A.Quantità: 2 disponibili
Aggiungi al carrelloPaperback. Condizione: Brand New. 200 pages. 9.00x7.00x0.50 inches. In Stock.
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Feature Engineering for Machine Learning (Paperback)
Editore: O'Reilly Media, Sebastopol, 2018
ISBN 10: 1491953241 ISBN 13: 9781491953242
Lingua: Inglese
Da: AussieBookSeller, Truganina, VIC, Australia
Valutazione del venditore 3 su 5 stelle
EUR 81,71
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, youundefinedll learn techniques for extracting and transforming featuresundefinedthe numeric representations of raw dataundefinedinto formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering.Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples.Youundefinedll examine:Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transformsNatural text techniques: bag-of-words, n-grams, and phrase detectionFrequency-based filtering and feature scaling for eliminating uninformative featuresEncoding techniques of categorical variables, including feature hashing and bin-countingModel-based feature engineering with principal component analysisThe concept of model stacking, using k-means as a featurization techniqueImage feature extraction with manual and deep-learning techniquesAbout the AuthorAlice is a technical leader in the field of Machine Learning. Her experience spans algorithm and platform development and applications. Currently, she is a Senior Manager in Amazon's Ad Platform. Previous roles include Director of Data Science at GraphLab/Dato/Turi, machine learning researcher at Microsoft Research, Redmond, and postdoctoral fellow at Carnegie Mellon University. She received a Ph.D. in Electrical Engineering and Computer science, and B.A. degrees in Computer Science in Mathematics, all from U.C. Berkeley. Principal Product Manager + Data Scientist for Concur Labs at SAP Concur, designing prototypes, interfaces and future tech for travel and expense. Amanda experiments with projects and programs to make machine learning more accessible. Her side projects include volunteering with the NASA Datanauts and getting outside as much as possible. Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, youll learn techniques for extracting and transforming featuresthe numeric representations of raw datainto formats for machine-learning models. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Feature Engineering for Machine Learning Models: Principles and Techniques for Data Scientists
Da: Revaluation Books, Exeter, Regno Unito
Valutazione del venditore 5 su 5 stelle
EUR 61,20
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Aggiungi al carrelloPaperback. Condizione: Brand New. 200 pages. 9.00x7.00x0.50 inches. In Stock. This item is printed on demand.
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Feature Engineering for Machine Learning
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
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EUR 79,69
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Aggiungi al carrelloPaperback / softback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
