Feature Engineering

Feature Engineering

Feature Engineering This course is part of multiple programs. Learn more Close This course is part of multiple programs Machine Learning on Google Cloud Specialization Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate OK Taught in English Instructor: Google Cloud Training Enroll Starts Mar 24 Financial aid available 33,353 already enrolled Included with


Feature engineering is a critical aspect of machine learning and data analysis that involves transforming raw data into a format that is more suitable for modeling and analysis. It is a key step in the data preprocessing pipeline and can greatly impact the performance of machine learning algorithms.

Feature engineering can involve a variety of techniques, including creating new features, transforming existing features, and selecting the most relevant features for the model. The goal is to extract meaningful information from the data and represent it in a way that is easily interpretable by machine learning algorithms.

One common technique in feature engineering is normalization, which involves scaling the features to a standard range or distribution. This can help to prevent certain features from dominating the model and improve its predictive performance.

Another important technique is feature selection, which involves selecting the most relevant features for the model while discarding irrelevant or redundant

Feature Engineering

This course is part of multiple programs.

Taught in English

Google Cloud Training

Instructor: Google Cloud Training

33,353 already enrolled

Included with Coursera Plus


Gain insight into a topic and learn the fundamentals


(1,748 reviews)



Intermediate level
Some related experience required
8 hours (approximately)
Flexible schedule
Learn at your own pace

What you’ll learn

  • Describe Vertex AI Feature Store and compare the key required aspects of a good feature.

  • Perform feature engineering using BigQuery ML, Keras, and TensorFlow.

  • Discuss how to preprocess and explore features with Dataflow and Dataprep.

  • Use tf.Transform.

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