Feature Engineering for Machine Learning Principles and Techniques for Data Scientists 1st Edition by Alice Zheng ,Amanda Casari ISBN 1491953241 978-1491953242

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Product details: 

ISBN 10:1491953241 

ISBN 13:978-1491953242

Author:Alice Zheng ,Amanda Casari

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

Table of contents: 

1. 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
2. the machine learning pipeline
   data
   tasks
   models
   features
   model evaluation
3. 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
4. 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
5. 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 test
   creating 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
6. 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
7. dimensionality reduction: squashing the data pancake with pca
   intuition
   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
8. nonlinear featurization via k-means model stacking
   k-means clustering
   clustering as surface tiling
   k-means featurization for classification
   alternative dense featurization
   pros, cons, and gotchas
   summary
   bibliography
9. 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
10. back to the feature: building an academic paper recommender
    item-based collaborative filtering
    first pass: data import, cleaning, and feature parsing
    academic paper recommender: naive approach
    second pass: more engineering and a smarter model

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Tags: Alice Zheng , Amanda Casari, Feature, Engineering