Coverart for item
The Resource Feature engineering for machine learning : principles and techniques for data scientists, Alice Zheng and Amanda Casari

Feature engineering for machine learning : principles and techniques for data scientists, Alice Zheng and Amanda Casari

Label
Feature engineering for machine learning : principles and techniques for data scientists
Title
Feature engineering for machine learning
Title remainder
principles and techniques for data scientists
Statement of responsibility
Alice Zheng and Amanda Casari
Creator
Contributor
Author
Subject
Genre
Language
eng
Cataloging source
N$T
http://library.link/vocab/creatorName
Zheng, Alice
Dewey number
006.3/1
Index
index present
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
Casari, Amanda
http://library.link/vocab/subjectName
  • Machine learning
  • Data mining
Label
Feature engineering for machine learning : principles and techniques for data scientists, Alice Zheng and Amanda Casari
Instantiates
Publication
Copyright
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
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
Control code
on1029545849
Dimensions
unknown
Edition
First edition
Extent
1 online resource
File format
unknown
Form of item
online
Isbn
9781491953198
Media category
computer
Media MARC source
rdamedia
Media type code
c
Quality assurance targets
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)1029545849
Label
Feature engineering for machine learning : principles and techniques for data scientists, Alice Zheng and Amanda Casari
Publication
Copyright
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
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
Control code
on1029545849
Dimensions
unknown
Edition
First edition
Extent
1 online resource
File format
unknown
Form of item
online
Isbn
9781491953198
Media category
computer
Media MARC source
rdamedia
Media type code
c
Quality assurance targets
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)1029545849

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