skip to content
Feature engineering for machine learning : principles and techniques for data scientists Preview this item
ClosePreview this item
Checking...

Feature engineering for machine learning : principles and techniques for data scientists

Author: Alice Zheng; Amanda Casari
Publisher: Sebastopol, CA : O'Reilly Media, Inc., ©2018 2018.
Edition/Format:   eBook : Document : English : First editionView all editions and formats
Summary:

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  Read more...

Rating:

(not yet rated) 0 with reviews - Be the first.

Subjects
More like this

Find a copy online

Find a copy in the library

&AllPage.SpinnerRetrieving; Finding libraries that hold this item...

Details

Genre/Form: Electronic books
Additional Physical Format: Print version:
Zheng, Alice.
Feature engineering for machine learning.
©2018
(OCoLC)957747646
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Alice Zheng; Amanda Casari
ISBN: 9781491953211 1491953217 9781491953198 1491953195
OCLC Number: 1029545849
Description: 1 online resource
Contents: Intro; Copyright; Table of Contents; Preface; Introduction; Conventions Used in This Book; Using Code Examples; O'Reilly Safari; How to Contact Us; Acknowledgments; Special Thanks from Alice; Special Thanks from Amanda; Chapter 1. The Machine Learning Pipeline; Data; Tasks; Models; Features; Model Evaluation; Chapter 2. Fancy Tricks with Simple Numbers; Scalars, Vectors, and Spaces; Dealing with Counts; Binarization; Quantization or Binning; Log Transformation; Log Transform in Action; Power Transforms: Generalization of the Log Transform; Feature Scaling or Normalization; Min-Max Scaling. Standardization (Variance Scaling)ℓ2 Normalization; Interaction Features; Feature Selection; Summary; Bibliography; Chapter 3. Text Data: Flattening, Filtering, and Chunking; Bag-of-X: Turning Natural Text into Flat Vectors; Bag-of-Words; Bag-of-n-Grams; Filtering for Cleaner Features; Stopwords; Frequency-Based Filtering; Stemming; Atoms of Meaning: From Words to n-Grams to Phrases; Parsing and Tokenization; Collocation Extraction for Phrase Detection; Summary; Bibliography; Chapter 4. The Effects of Feature Scaling: From Bag-of-Words to Tf-Idf; Tf-Idf : A Simple Twist on Bag-of-Words. Putting It to the TestCreating a Classification Dataset; Scaling Bag-of-Words with Tf-Idf Transformation; Classification with Logistic Regression; Tuning Logistic Regression with Regularization; Deep Dive: What Is Happening?; Summary; Bibliography; Chapter 5. Categorical Variables: Counting Eggs in the Age of Robotic Chickens; Encoding Categorical Variables; One-Hot Encoding; Dummy Coding; Effect Coding; Pros and Cons of Categorical Variable Encodings; Dealing with Large Categorical Variables; Feature Hashing; Bin Counting; Summary; Bibliography. Chapter 6. Dimensionality Reduction: Squashing the Data Pancake with PCAIntuition; Derivation; Linear Projection; Variance and Empirical Variance; Principal Components: First Formulation; Principal Components: Matrix-Vector Formulation; General Solution of the Principal Components; Transforming Features; Implementing PCA; PCA in Action; Whitening and ZCA; Considerations and Limitations of PCA; Use Cases; Summary; Bibliography; Chapter 7. Nonlinear Featurization via K-Means Model Stacking; k-Means Clustering; Clustering as Surface Tiling; k-Means Featurization for Classification. Alternative Dense FeaturizationPros, Cons, and Gotchas; Summary; Bibliography; Chapter 8. Automating the Featurizer: Image Feature Extraction and Deep Learning; The Simplest Image Features (and Why They Don't Work); Manual Feature Extraction: SIFT and HOG; Image Gradients; Gradient Orientation Histograms; SIFT Architecture; Learning Image Features with Deep Neural Networks; Fully Connected Layers; Convolutional Layers; Rectified Linear Unit (ReLU) Transformation; Response Normalization Layers; Pooling Layers; Structure of AlexNet; Summary; Bibliography.
Responsibility: Alice Zheng and Amanda Casari.

Reviews

User-contributed reviews
Retrieving GoodReads reviews...
Retrieving DOGObooks reviews...

Tags

Be the first.

Similar Items

Confirm this request

You may have already requested this item. Please select Ok if you would like to proceed with this request anyway.

Linked Data


Primary Entity

<http://www.worldcat.org/oclc/1029545849> # Feature engineering for machine learning : principles and techniques for data scientists
    a schema:Book, schema:MediaObject, schema:CreativeWork ;
    library:oclcnum "1029545849" ;
    library:placeOfPublication <http://id.loc.gov/vocabulary/countries/cau> ;
    rdfs:comment "Warning: This malformed URI has been treated as a string - 'https://img1.od-cdn.com/ImageType-100/2858-1/{FAE3F32C-8E89-4DBF-87D3-9D80869009D9}Img100.jpg'" ;
    schema:about <http://experiment.worldcat.org/entity/work/data/4881005300#Topic/computers_general> ; # COMPUTERS--General
    schema:about <http://dewey.info/class/006.31/e23/> ;
    schema:about <http://experiment.worldcat.org/entity/work/data/4881005300#Topic/machine_learning> ; # Machine learning
    schema:about <http://experiment.worldcat.org/entity/work/data/4881005300#Topic/data_mining> ; # Data mining
    schema:author <http://experiment.worldcat.org/entity/work/data/4881005300#Person/zheng_alice> ; # Alice Zheng
    schema:author <http://experiment.worldcat.org/entity/work/data/4881005300#Person/casari_amanda> ; # Amanda Casari
    schema:bookEdition "First edition." ;
    schema:bookFormat schema:EBook ;
    schema:copyrightYear "2018" ;
    schema:datePublished "2018" ;
    schema:description "Intro; Copyright; Table of Contents; Preface; Introduction; Conventions Used in This Book; Using Code Examples; O'Reilly Safari; How to Contact Us; Acknowledgments; Special Thanks from Alice; Special Thanks from Amanda; Chapter 1. The Machine Learning Pipeline; Data; Tasks; Models; Features; Model Evaluation; Chapter 2. Fancy Tricks with Simple Numbers; Scalars, Vectors, and Spaces; Dealing with Counts; Binarization; Quantization or Binning; Log Transformation; Log Transform in Action; Power Transforms: Generalization of the Log Transform; Feature Scaling or Normalization; Min-Max Scaling."@en ;
    schema:exampleOfWork <http://worldcat.org/entity/work/id/4881005300> ;
    schema:genre "Electronic books"@en ;
    schema:inLanguage "en" ;
    schema:isSimilarTo <http://www.worldcat.org/oclc/957747646> ;
    schema:name "Feature engineering for machine learning : principles and techniques for data scientists"@en ;
    schema:productID "1029545849" ;
    schema:url <http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=1738376> ;
    schema:url <https://www.overdrive.com/search?q=FAE3F32C-8E89-4DBF-87D3-9D80869009D9> ;
    schema:url <http://public.ebookcentral.proquest.com/choice/publicfullrecord.aspx?p=5328406> ;
    schema:url <http://www.vlebooks.com/vleweb/product/openreader?id=none&isbn=9781491953211> ;
    schema:url <http://public.eblib.com/choice/publicfullrecord.aspx?p=5328406> ;
    schema:url <https://samples.overdrive.com/?crid=fae3f32c-8e89-4dbf-87d3-9d80869009d9&.epub-sample.overdrive.com> ;
    schema:url "https://img1.od-cdn.com/ImageType-100/2858-1/{FAE3F32C-8E89-4DBF-87D3-9D80869009D9}Img100.jpg" ;
    schema:workExample <http://worldcat.org/isbn/9781491953198> ;
    schema:workExample <http://worldcat.org/isbn/9781491953211> ;
    wdrs:describedby <http://www.worldcat.org/title/-/oclc/1029545849> ;
    .


Related Entities

<http://experiment.worldcat.org/entity/work/data/4881005300#Person/casari_amanda> # Amanda Casari
    a schema:Person ;
    schema:familyName "Casari" ;
    schema:givenName "Amanda" ;
    schema:name "Amanda Casari" ;
    .

<http://experiment.worldcat.org/entity/work/data/4881005300#Person/zheng_alice> # Alice Zheng
    a schema:Person ;
    schema:familyName "Zheng" ;
    schema:givenName "Alice" ;
    schema:name "Alice Zheng" ;
    .

<http://experiment.worldcat.org/entity/work/data/4881005300#Topic/computers_general> # COMPUTERS--General
    a schema:Intangible ;
    schema:name "COMPUTERS--General"@en ;
    .

<http://worldcat.org/isbn/9781491953198>
    a schema:ProductModel ;
    schema:isbn "1491953195" ;
    schema:isbn "9781491953198" ;
    .

<http://worldcat.org/isbn/9781491953211>
    a schema:ProductModel ;
    schema:isbn "1491953217" ;
    schema:isbn "9781491953211" ;
    .

<http://www.worldcat.org/oclc/957747646>
    a schema:CreativeWork ;
    rdfs:label "Feature engineering for machine learning." ;
    schema:description "Print version:" ;
    schema:isSimilarTo <http://www.worldcat.org/oclc/1029545849> ; # Feature engineering for machine learning : principles and techniques for data scientists
    .

<http://www.worldcat.org/title/-/oclc/1029545849>
    a genont:InformationResource, genont:ContentTypeGenericResource ;
    schema:about <http://www.worldcat.org/oclc/1029545849> ; # Feature engineering for machine learning : principles and techniques for data scientists
    schema:dateModified "2019-05-11" ;
    void:inDataset <http://purl.oclc.org/dataset/WorldCat> ;
    .


Content-negotiable representations

Close Window

Please sign in to WorldCat 

Don't have an account? You can easily create a free account.