Essential math data science de nield thomas (45 resultados)
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Librería: HPB-Red, Dallas, TX, Estados Unidos de AmericaHPB-Red
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EUR 20,08
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paperback. Condición: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.
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EUR 29,43
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paperback. Condición: Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority.
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Librería: HPB-Ruby, Dallas, TX, Estados Unidos de AmericaHPB-Ruby
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EUR 29,43
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paperback. Condición: Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority.
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- Primera edición
Librería: CollegePoint, Inc, Jamestown, TN, Estados Unidos de AmericaCollegePoint, Inc
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EUR 27,84
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Paperback. Condición: Good. 1st Edition. We only honor returns for quality issues and won't accept reasons such as 'change my mind', 'find a better price', or 'school book requirement change', etc.
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Librería: Jadewalky Book Company, HANOVER PARK, IL, Estados Unidos de AmericaJadewalky Book Company
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EUR 39,24
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Condición: Used - Very Good. Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Al…ong the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career.Learn how to: Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance Manipulate vectors and matrices and perform matrix decomposition Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market.
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
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EUR 41,16
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Librería: Lakeside Books, Benton Harbor, MI, Estados Unidos de AmericaLakeside Books
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EUR 39,97
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Condición: 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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Librería: Rarewaves USA, OSWEGO, IL, Estados Unidos de AmericaRarewaves USA
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EUR 44,54
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Paperback. Condición: New. To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus.Practical examp…les with Python code will help you see how the math applies to the work you'll be doing, providing a clear understanding of how concepts work under the hood while connecting them to applications like machine learning. You'll get a solid foundation in the math essential for data science, but more importantly, you'll be able to use it to:Recognize the nuances and pitfalls of probability mathMaster statistics and hypothesis testing (and avoid common pitfalls)Discover practical applications of probability, statistics, calculus, and machine learningIntuitively understand linear algebra as a transformation of space, not just grids of numbers being multiplied and addedPerform calculus derivatives and integrals completely from scratch in PythonApply what you've learned to machine learning, including linear regression, logistic regression, and neural networks.
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Librería: BargainBookStores, Grand Rapids, MI, Estados Unidos de AmericaBargainBookStores
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EUR 44,78
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Paperback or Softback. Condición: New. Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics. Book.
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Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de AmericaPBShop.store US
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EUR 45,92
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PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.
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Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de AmericaLucky's Textbooks
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EUR 42,96
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
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EUR 44,85
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Librería: PBShop.store UK, Fairford, GLOS, Reino UnidoPBShop.store UK
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EUR 42,89
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PAP. Condición: Used - Very Good. Used - Like New Book. Shipped from UK. Established seller since 2000.
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Librería: PBShop.store UK, Fairford, GLOS, Reino UnidoPBShop.store UK
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EUR 42,89
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PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.
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Librería: Rarewaves.com USA, London, LONDO, Reino UnidoRarewaves.com USA
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EUR 51,93
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Paperback. Condición: New. To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus.Practical examp…les with Python code will help you see how the math applies to the work you'll be doing, providing a clear understanding of how concepts work under the hood while connecting them to applications like machine learning. You'll get a solid foundation in the math essential for data science, but more importantly, you'll be able to use it to:Recognize the nuances and pitfalls of probability mathMaster statistics and hypothesis testing (and avoid common pitfalls)Discover practical applications of probability, statistics, calculus, and machine learningIntuitively understand linear algebra as a transformation of space, not just grids of numbers being multiplied and addedPerform calculus derivatives and integrals completely from scratch in PythonApply what you've learned to machine learning, including linear regression, logistic regression, and neural networks.
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Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail
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EUR 52,24
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Paperback. Condición: new. Paperback. To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus.Prac…tical examples with Python code will help you see how the math applies to the work you'll be doing, providing a clear understanding of how concepts work under the hood while connecting them to applications like machine learning. You'll get a solid foundation in the math essential for data science, but more importantly, you'll be able to use it to:Recognize the nuances and pitfalls of probability mathMaster statistics and hypothesis testing (and avoid common pitfalls)Discover practical applications of probability, statistics, calculus, and machine learningIntuitively understand linear algebra as a transformation of space, not just grids of numbers being multiplied and addedPerform calculus derivatives and integrals completely from scratch in PythonApply what you've learned to machine learning, including linear regression, logistic regression, and neural networks To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Librería: California Books, Miami, FL, Estados Unidos de AmericaCalifornia Books
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EUR 52,63
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Librería: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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EUR 47,44
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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EUR 42,87
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Librería: Ria Christie Collections, Uxbridge, Reino UnidoRia Christie Collections
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EUR 50,66
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Librería: Chiron Media, Wallingford, Reino UnidoChiron Media
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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EUR 50,90
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Librería: GoldBooks, Denver, CO, Estados Unidos de AmericaGoldBooks
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EUR 67,73
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Paperback. Condición: new. New Copy. Customer Service Guaranteed.
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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
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EUR 70,58
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Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
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EUR 65,82
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Paperback. Condición: Brand New. 350 pages. 9.19x7.00x0.73 inches. In Stock.
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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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EUR 78,20
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Condición: New. 1st edition NO-PA16APR2015-KAP.
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Librería: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrlandaKennys Bookshop and Art Galleries Ltd.
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EUR 72,70
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Condición: New. 2022. Paperback. . . . . .
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Librería: Rarewaves USA United, OSWEGO, IL, Estados Unidos de AmericaRarewaves USA United
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EUR 46,50
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Paperback. Condición: New. To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus.Practical examp…les with Python code will help you see how the math applies to the work you'll be doing, providing a clear understanding of how concepts work under the hood while connecting them to applications like machine learning. You'll get a solid foundation in the math essential for data science, but more importantly, you'll be able to use it to:Recognize the nuances and pitfalls of probability mathMaster statistics and hypothesis testing (and avoid common pitfalls)Discover practical applications of probability, statistics, calculus, and machine learningIntuitively understand linear algebra as a transformation of space, not just grids of numbers being multiplied and addedPerform calculus derivatives and integrals completely from scratch in PythonApply what you've learned to machine learning, including linear regression, logistic regression, and neural networks.
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 66,50
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Taschenbuch. Condición: Neu. Neuware -Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural net…works. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career. Learn how to: - Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning - Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon - Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance - Manipulate vectors and matrices and perform matrix decomposition - Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks - Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market Englisch.
