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Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists

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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
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Python Data Analysis
Python Data Analysis

Key Features

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  • Perform advanced, high-performance linear algebra and mathematical calculations with clean and efficient Python code
  • An easy-to-follow guide with realistic examples that are frequently used in real-world data...
Python Data Science Handbook: Essential Tools for Working with Data
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For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib,...

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Make an app from start to finish on your own or with a dedicated team. This book is your all-in-one, go-to resource for designing, building, and marketing, a trending app that others flock to buy. Use detailed analysis to decide what designs you should choose and whether you should learn to code or hire someone else to do the trench...


Think Python
Think Python
In January 1999 I was preparing to teach an introductory programming class in Java. I had taught it three times and I was getting frustrated. The failure rate in the class was too high and, even for students who succeeded, the overall level of achievement was too low.

One of the problems I saw was the books. They were too
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Big Data Analysis with Python: Combine Spark and Python to unlock the powers of parallel computing and machine learning
Big Data Analysis with Python: Combine Spark and Python to unlock the powers of parallel computing and machine learning

Get to grips with processing large volumes of data and presenting it as engaging, interactive insights using Spark and Python.

Key Features

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Python for Data Mining Quick Syntax Reference
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?Learn how to use Python and its structures, how to install Python, and which tools are best suited for data analyst work. This book provides you with a handy reference and tutorial on topics ranging from basic Python concepts through to data mining, manipulating and importing datasets, and data analysis.

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