Feature Engineering Machine Learning (138 results)
- Softcover
Seller: HPB-Red, Dallas, TX, U.S.A.HPB-Red
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paperback. Condition: 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.
- Softcover
Seller: ThriftBooks-Dallas, Dallas, TX, U.S.A.ThriftBooks-Dallas
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Paperback. Condition: Very Good. No Jacket. Former library book; May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.
- Softcover
Seller: Vedams eBooks (P) Ltd, New Delhi, IndiaVedams eBooks (P) Ltd
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Soft cover. Condition: New. This book begins with an introduction to Data Science followed by the Python concepts. The readers will understand how to interact with various database and Statistics concepts with their Python implementations. You will learn how to import various types of data in Python, which is the first step of t…he data analysis process. Once you become comfortable with data importing, you will clean the dataset and after that will gain an understanding about various visualization charts. This book focuses on how to apply feature engineering techniques to make your data more valuable to an algorithm. The readers will get to know various Machine Learning Algorithms, concepts, Time Series data, and a few real-world case studies. This book also presents some best practices that will help you to be industry-ready. This book focuses on how to practice data science techniques while learning their concepts using Python and Jupyter. This book is a complete answer to the most common question that how can you get started with Data Science instead of explaining Mathematics and Statistics behind the Machine Learning Algorithms.
- Softcover
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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Condition: New. In.
- Softcover
Seller: Blue Vase Books, Interlochen, MI, U.S.A.Blue Vase Books
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Condition: good. The item shows wear from consistent use, but it remains in good condition and works perfectly. All pages and cover are intact including the dust cover, if applicable . Spine may show signs of wear. Pages may include limited notes and highlighting. May NOT include discs, access code or other supplemental material…s.
- Softcover
Seller: medimops, Berlin, Germanymedimops
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US$ 30.63
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Condition: very good. Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages.
- Softcover
Seller: BargainBookStores, Grand Rapids, MI, U.S.A.BargainBookStores
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Paperback or Softback. Condition: New. High-performance Algorithmic Trading using Machine Learning: Building automated trading strategies with AutoML and feature engineering (English Editio. Book.
- Softcover
Seller: California Books, Miami, FL, U.S.A.California Books
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Language: English
Published by Independently published 2025
Series: Comprehensive Options 2025, Book 2 of 4. Book 2 of 4 - Comprehensive Options 2025
- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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US$ 47.08
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Condition: As New. Unread book in perfect condition.
Language: English
Published by Independently published 2025
Series: Comprehensive Options 2025, Book 2 of 4. Book 2 of 4 - Comprehensive Options 2025
- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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- Softcover
Seller: California Books, Miami, FL, U.S.A.California Books
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- Softcover
Seller: California Books, Miami, FL, U.S.A.California Books
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- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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- Softcover
Seller: California Books, Miami, FL, U.S.A.California Books
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Published by Packt Publishing 2024
- Softcover
Seller: Evergreen Goodwill, Seattle, WA, U.S.A.Evergreen Goodwill
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paperback. Condition: Good.
- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Condition: As New. Unread book in perfect condition.
- Softcover
Seller: PBShop.store UK, Fairford, GLOS, United KingdomPBShop.store UK
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US$ 49.76
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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
- Softcover
Seller: medimops, Berlin, Germanymedimops
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Condition: as new. Wie neu/Like new.
- Softcover
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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US$ 55.34
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Paperback. Condition: 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, youll learn techniques for extracting and transforming featuresthe numeric representations of raw datainto 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.Youll 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.
- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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US$ 52.71
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Condition: New.
Language: English
Published by Cambridge University Press 2020
Series: Essentials for Machine Learning, Book 1 of 1. Book 1 of 1 - Essentials for Machine Learning
- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Condition: As New. Unread book in perfect condition.
- Softcover
Seller: World of Books (was SecondSale), Montgomery, IL, U.S.A.World of Books (was SecondSale)
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US$ 55.57
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Condition: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc.
- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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- Softcover
Seller: BargainBookStores, Grand Rapids, MI, U.S.A.BargainBookStores
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US$ 55.59
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Paperback or Softback. Condition: New. Python Feature Engineering Cookbook - Second Edition: Over 70 recipes for creating, engineering, and transforming features to build machine learning m. Book.
- Softcover
Seller: medimops, Berlin, Germanymedimops
Contact seller5-star sellerCondition: Used - Very good
US$ 45.43
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Condition: very good. Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages.
- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
Contact seller5-star sellerCondition: Used - As new
US$ 53.58
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Condition: As New. Unread book in perfect condition.
- Softcover
Seller: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US
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US$ 56.26
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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
- Softcover
Seller: California Books, Miami, FL, U.S.A.California Books
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Condition: New.
- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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US$ 55.08
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Condition: As New. Unread book in perfect condition.
- Softcover
Seller: Rarewaves.com USA, London, LONDO, United KingdomRarewaves.com USA
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US$ 60.86
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Paperback. Condition: New. Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you'll 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. 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. You'll examine: Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transforms Natural text techniques: bag-of-words, n-grams, and phrase detection Frequency-based filtering and feature scaling for eliminating uninformative features Encoding techniques of categorical variables, including feature hashing and bin-counting Model-based feature engineering with principal component analysis The concept of model stacking, using k-means as a featurization technique Image feature extraction with manual and deep-learning techniques.
