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Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications (Addison-Wesley Data & Analytics Series) - Tapa blanda
Sinopsis
The typical data science task in industry starts with an “ask” from the business. But few data scientists have been taught what to do with that ask. This book shows them how to assess it in the context of the business’s goals, reframe it to work optimally for both the data scientist and the employer, and then execute on it. Written by two of the experts who’ve achieved breakthrough optimizations at BuzzFeed, it’s packed with real-world examples that take you from start to finish: from ask to actionable insight.
Andrew Kelleher and Adam Kelleher walk you through well-formed, concrete principles for approaching common data science problems, giving you an easy-to-use checklist for effective execution. Using their principles and techniques, you’ll gain deeper understanding of your data, learn how to analyze noise and confounding variables so they don’t compromise your analysis, and save weeks of iterative improvement by planning your projects more effectively upfront.
Once you’ve mastered their principles, you’ll put them to work in two realistic, beginning-to-end site optimization tasks. These extended examples come complete with reusable code examples and recommended open-source solutions designed for easy adaptation to your everyday challenges. They will be especially valuable for anyone seeking their first data science job -- and everyone who’s found that job and wants to succeed in it.
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Acerca del autor
Andrew Kelleher is a staff software engineer and distributed systems architect at Venmo. He was previously a staff software engineer at BuzzFeed and has worked on data pipelines and algorithm implementations for modern optimization. He graduated with a BS in physics from Clemson University. He runs a meetup in New York City that studies the fundamentals behind distributed systems in the context of production applications, and was ranked one of FastCompany's most creative people two years in a row.
Adam Kelleher wrote this book while working as principal data scientist at BuzzFeed and adjunct professor at Columbia University in the City of New York. As of May 2018, he is chief data scientist for research at Barclays and teaches causal inference and machine learning products at Columbia. He graduated from Clemson University with a BS in physics, and has a PhD in cosmology from University of North Carolina at Chapel Hill.
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Machine Learning in Production : Developing and Optimizing Data Science Workflows and Applications
Publicado por
Addison Wesley Professional, 2019
ISBN 10: 0134116542
ISBN 13: 9780134116549
Antiguo o usado
Tapa blanda
Librería: Better World Books: West, Reno, NV, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: Very Good. Used book that is in excellent condition. May show signs of wear or have minor defects. Nº de ref. del artículo: 52230923-75
Cantidad disponible: 1 disponibles
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Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications (Addison-Wesley Data & Analytics Series)
Publicado por
Addison-Wesley Professional, 2019
ISBN 10: 0134116542
ISBN 13: 9780134116549
Antiguo o usado
Tapa blanda
Librería: SecondSale, Montgomery, IL, Estados Unidos de America
Calificación del vendedor: 4 de 5 estrellas
Condición: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc. Nº de ref. del artículo: 00087485999
Cantidad disponible: 1 disponibles
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Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications (Addison-Wesley Data & Analytics Series)
Publicado por
Addison-Wesley Professional, 2019
ISBN 10: 0134116542
ISBN 13: 9780134116549
Nuevo
Softcover
Librería: booksdeck, Westlake Village, CA, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Softcover. Condición: New. Brand new book. International edition printed overseas but with similar contents as compared to the US edition. Same day shipping with tracking. Nº de ref. del artículo: 105|9780134116549
Cantidad disponible: 2 disponibles
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Machine Learning In Production
Publicado por
Addison-Wesley Professional, 2019
ISBN 10: 0134116542
ISBN 13: 9780134116549
Nuevo
Tapa blanda
Librería: Romtrade Corp., STERLING HEIGHTS, MI, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide. Nº de ref. del artículo: ABNR-25918
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Machine Learning in Production
Librería: Books Puddle, New York, NY, Estados Unidos de America
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Condición: New. 1st Edition. Nº de ref. del artículo: 26376292738
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Machine Learning in Production
Librería: Biblios, Frankfurt am main, HESSE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: 18376292744
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Applied Machine Learning for Data Scientists and Software Engineers: Framing - the First Steps Toward Successful Execution
Publicado por
Addison-Wesley Professional, 2019
ISBN 10: 0134116542
ISBN 13: 9780134116549
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Librería: Revaluation Books, Exeter, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: Brand New. 255 pages. 9.00x7.00x0.50 inches. In Stock. Nº de ref. del artículo: zk0134116542
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Machine Learning in Production (Paperback)
Publicado por
Pearson Education (US), Boston, 2019
ISBN 10: 0134116542
ISBN 13: 9780134116549
Nuevo
Paperback
Librería: AussieBookSeller, Truganina, VIC, Australia
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: new. Paperback. Machine Learning in Production is a crash course in data science and machine learning for people who need to solve real-world problems in production environments. Written for technically competent accidental data scientists with more curiosity and ambition than formal training, this complete and rigorous introduction stresses practice, not theory. Building on agile principles, Andrew and Adam Kelleher show how to quickly deliver significant value in production, resisting overhyped tools and unnecessary complexity. Drawing on their extensive experience, they help you ask useful questions and then execute production projects from start to finish. The authors show just how much information you can glean with straightforward queries, aggregations, and visualisations, and they teach indispensable error analysis methods to avoid costly mistakes. They turn to workhorse machine learning techniques such as linear regression, classification, clustering, and Bayesian inference, helping you choose the right algorithm for each production problem. Their concluding section on hardware, infrastructure, and distributed systems offers unique and invaluable guidance on optimisation in production environments. Andrew and Adam always focus on what matters in production: solving the problems that offer the highest return on investment, using the simplest, lowest-risk approaches that work. Leverage agile principles to maximise development efficiency in production projects Learn from practical Python code examples and visualisations that bring essential algorithmic concepts to life Start with simple heuristics and improve them as your data pipeline matures Avoid bad conclusions by implementing foundational error analysis techniques Communicate your results with basic data visualisation techniques Master basic machine learning techniques, starting with linear regression and random forests Perform classification and clustering on both vector and graph data Learn the basics of graphical models and Bayesian inference Understand correlation and causation in machine learning models Explore overfitting, model capacity, and other advanced machine learning techniques Make informed architectural decisions about storage, data transfer, computation, and communication Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Nº de ref. del artículo: 9780134116549
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Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications (Addison-Wesley Data & Analytics Series)
Publicado por
Addison-Wesley Professional, 2019
ISBN 10: 0134116542
ISBN 13: 9780134116549
Antiguo o usado
Tapa blanda
Librería: Goodwill of Silicon Valley, SAN JOSE, CA, Estados Unidos de America
Calificación del vendedor: 4 de 5 estrellas
Condición: very_good. Supports Goodwill of Silicon Valley job training programs. The cover and pages are in very good condition! The cover and any other included accessories are also in very good condition showing some minor use. The spine is straight, there are no rips tears or creases on the cover or the pages. Nº de ref. del artículo: GWSVV.0134116542.VG
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Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications
Publicado por
Addison-Wesley Professional, 2019
ISBN 10: 0134116542
ISBN 13: 9780134116549
Nuevo
Tapa blanda
Librería: Basi6 International, Irving, TX, Estados Unidos de America
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Condición: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. Nº de ref. del artículo: ABEJUNE24-417
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