This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.
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Feature Engineering
Ce cours fait partie de plusieurs programmes.
Instructeur : Google Cloud Training
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Ce que vous apprendrez
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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Il y a 8 modules dans ce cours
This module provides an overview of the course and its objectives.
Inclus
1 vidéo
This module introduces Vertex AI Feature Store.
Inclus
6 vidéos1 lecture1 devoir
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.
Inclus
9 vidéos1 lecture1 devoir
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.
Inclus
12 vidéos1 lecture1 devoir3 éléments d'application
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.
Inclus
3 vidéos1 lecture1 devoir
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.
Inclus
5 vidéos1 lecture1 devoir
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.
Inclus
5 vidéos1 lecture1 devoir
This module is a summary of the Feature Engineering course.
Inclus
4 lectures
Instructeur
Offert par
Recommandé si vous êtes intéressé(e) par Machine Learning
DeepLearning.AI
Imperial College London
Pour quelles raisons les étudiants sur Coursera nous choisissent-ils pour leur carrière ?
Avis des étudiants
1 771 avis
- 5 stars
63,46 %
- 4 stars
24,90 %
- 3 stars
7,39 %
- 2 stars
2,14 %
- 1 star
2,08 %
Affichage de 3 sur 1771
Révisé le 30 avr. 2020
The course was very informative, but I think that there are opportunities for the student to have to figure out how to do more portions of the labs.
Révisé le 11 nov. 2018
Great topics, the instructions are great. The only suggestion is cut down the number of different videos. just combine them together. 15 3 mins is not a great experience vs 3 15 min videos.
Révisé le 20 avr. 2019
faced multiple issues
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Last Updated: September 1, 2015
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.