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Mastering Feature Engineering Principles and Techniques for Data Scientists

Langue : Anglais

Auteur :

Couverture de l’ouvrage Mastering Feature Engineering
Feature engineering is essential to applied machine learning, but using domain knowledge to strengthen your predictive models can be difficult and expensive. To help fill the information gap on feature engineering, this complete hands-on guide teaches beginning-to-intermediate data scientists how to work with this widely practiced but little discussed topic.

Author Alice Zheng explains common practices and mathematical principles to help engineer features for new data and tasks. If you understand basic machine learning concepts like supervised and unsupervised learning, you’re ready to get started. Not only will you learn how to implement feature engineering in a systematic and principled way, you’ll also learn how to practice better data science.
- Chapter 1Introduction
- Chapter 2 - Fancy Tricks with Simple Numbers
- Chapter 3 - Basic Feature Engineering for Text Data: Flatten and Filter
- Chapter 4 - The Effects of Feature Scaling: From Bag-of-Words to Tf-Idf
- Chapter 5 - Counts and Categorical Variables: Counting Eggs in the Age of Robotic Chickens
- Chapter 6 - Dimensionality Reduction: Squashing the Data Pancake with PCA
- Chapter 7 - Non-Linear Featurization and Model Stacking
- Chapter 8 - Automating the Featurizer: Image Feature Extraction and Deep Learning
- Appendix A - Linear Modeling and Linear Algebra Basics
Alice Zheng is a technical leader in the field of Machine Learning. Her experience spans algorithm and platform development and applications. Currently, she is a Senior Manager in Amazon's Ad Platform.

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Ouvrage de 200 p.

Disponible chez l'éditeur (délai d'approvisionnement : 12 jours).

Prix indicatif 67,11 €

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