feature engineering machine learning von zheng alice (30 Ergebnisse)

Paperback. Zustand: Very Good. 1. Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported.

Zustand: good. Befriedigend/Good: Durchschnittlich erhaltenes Buch bzw. Schutzumschlag mit Gebrauchsspuren, aber vollständigen Seiten. / Describes the average WORN book or dust jacket that has all the pages present.

Zustand: 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.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: Very Good. Item in very good condition! Textbooks may not include supplemental items i.e. CDs, access codes etc.

Zustand: New. In.

Paperback / softback. Zustand: New. New copy - Usually dispatched within 4 working days. 490.

Zustand: 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. . . . . .

Paperback. Zustand: 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.

Zustand: New.

Zustand: New.

Paperback. Zustand: 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.

Zustand: As New. Unread book in perfect condition.

Zustand: As New. Unread book in perfect condition.

Paperback. Zustand: 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.

Taschenbuch. Zustand: 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.

Zustand: 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.

Paperback. Zustand: 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.

Zustand: New.

Zustand: New.

Zustand: New.

Zustand: New. New Book.

Paperback. Zustand: Brand New. 200 pages. 9.00x7.00x0.50 inches. In Stock.

Paperback. Zustand: 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.

Paperback. Zustand: 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!

Paperback. Zustand: 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.

Zustand: New.

Paperback. Zustand: Brand New. 200 pages. 9.00x7.00x0.50 inches. In Stock. This item is printed on demand.

Paperback / softback. Zustand: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.