Google Cloud

Feature Engineering

This course is part of multiple programs.

Taught in English

34,075 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.5

(1,760 reviews)

|

87%

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.

Details to know

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Assessments

6 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.5

(1,760 reviews)

|

87%

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

See how employees at top companies are mastering in-demand skills

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There are 8 modules in this course

This module provides an overview of the course and its objectives.

What's included

1 video

This module introduces Vertex AI Feature Store.

What's included

6 videos1 reading1 quiz

Feature engineering is often the longest and most difficult phase of building your ML project. In the feature engineering process, you start with your raw data and use your own domain knowledge to create features that will make your machine learning algorithms work. In this module we explore what makes a good feature and how to represent them in your ML model.

What's included

9 videos1 reading1 quiz

This module reviews the differences between machine learning and statistics, and how to perform feature engineering in both BigQuery ML and Keras. We'll also cover some advanced feature engineering practices.

What's included

12 videos1 reading1 quiz3 app items

In this module you will learn more about Dataflow, which is a complementary technology to Apache Beam and both of them can help you build and run preprocessing and feature engineering.

What's included

3 videos1 reading1 quiz

In traditional machine learning, feature crosses don’t play much of a role, but in modern day ML methods, feature crosses are an invaluable part of your toolkit. In this module, you will learn how to recognize the kinds of problems where feature crosses are a powerful way to help machines learn.

What's included

5 videos1 reading1 quiz

TensorFlow Transform (tf.Transform) is a library for preprocessing data with TensorFlow. tf.Transform is useful for preprocessing that requires a full pass the data, such as: - normalizing an input value by mean and stdev - integerizing a vocabulary by looking at all input examples for values - bucketizing inputs based on the observed data distribution In this module we will explore use cases for tf.Transform.

What's included

5 videos1 reading1 quiz

This module is a summary of the Feature Engineering course.

What's included

4 readings

Instructor

Instructor ratings
4.5 (88 ratings)
Google Cloud Training
Google Cloud
1,472 Courses2,609,219 learners

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Google Cloud

Recommended if you're interested in Machine Learning

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