January 2025
Intermediate to advanced
436 pages
11h 10m
English
Content preview from Feature Engineering for Modern Machine Learning with Scikit-LearnBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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7.3 Practical Exercises: Chapter 7
In this exercise section, we’ll apply feature engineering techniques within TensorFlow/Keras pipelines, focusing on preparing data, handling preprocessing layers, and constructing end-to-end workflows with tf.data. These exercises are designed to reinforce the concepts of embedding preprocessing steps into the model pipeline.
Exercise 1: Normalizing and Encoding Data Using Keras Preprocessing Layers
Objective: Use Keras preprocessing layers to normalize numeric data and one-hot encode categorical data within a model pipeline.
Instructions:
- Create a small dataset with two numeric and one categorical feature.
- Apply the Normalization layer for the numeric data and the StringLookup with CategoryEncoding layer for the ...
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