We may earn an affiliate commission when you visit our partners.
Google Cloud
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.
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.
This course is no longer available. Find something similar by browsing:
Vertex AI Feature Store
Feature Engineering
Machine Learning Models
Data Columns
BigQuery ML
Keras
TensorFlow
Register for this course and see more details by visiting:
OpenCourser.com/course/fs21lb/feature
What's inside
Syllabus
Introduction
Introduction to Vertex AI Feature Store
Raw Data to Features
Feature Engineering
Read more
Syllabus
Introduction
Introduction to Vertex AI Feature Store
Raw Data to Features
Feature Engineering
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Develops an understanding of the benefits of using Vertex AI Feature Store
Provides examples of how to improve the accuracy of ML models
Explores techniques for identifying highly relevant data
Provides hands-on labs for practical implementation
Features a range of content for various learning styles
Requires prior knowledge in BigQuery ML, Keras, and TensorFlow
Save this course
Create your own learning path.
Save this course to your list so you can find it easily later.
Save
Save
Reviews summary
Practical feature engineering for ml professionals
According to learners, this course provides a solid foundation in Feature Engineering, particularly highlighting its strong focus on the highly relevant Vertex AI Feature Store. Students praise the practical labs and hands-on experience with tools like BigQuery ML, Keras, and TensorFlow, which facilitate immediate application. While many appreciate the clear explanations, some experienced learners found the course lacks advanced depth for niche problems, with certain sections feeling fast-paced. Overall, it's considered a valuable resource for those looking to improve ML model accuracy, though a few reported occasional lab issues or outdated content in older reviews.
Generally clear explanations, but some sections can feel rushed.
"The TensorFlow Transform section was a bit fast-paced and could use more in-depth examples."
"I found some of the explanations to be too high-level, particularly for TensorFlow Transform."
"The concepts are explained clearly, but sometimes the depth felt lacking for implementing features in production."
Provides a strong and clear introduction to feature engineering.
"This course provided me with a solid foundation for feature engineering."
"I gained a good introduction to feature engineering concepts and their application in Vertex AI."
"I found this an excellent course for getting started with feature engineering on GCP."
Offers valuable hands-on experience and immediate applicability.
"The labs using BigQuery ML were well-structured and provided crucial hands-on experience."
"Absolutely loved the practical focus! The labs were excellent and reinforced the theory perfectly."
"It really helped me apply concepts immediately at work, the practical focus was a true gem."
Highlights a cutting-edge, highly relevant topic for MLOps.
"The content on Vertex AI Feature Store was incredibly insightful and practical."
"The most impactful part was the introduction to Vertex AI Feature Store – truly cutting-edge and highly relevant for MLOps."
"The Vertex AI Feature Store section alone is worth the price of admission for me."
Occasional reports of lab environment issues and outdated content.
"I encountered some issues with the lab environments occasionally."
"Outdated information in some parts of the BigQuery ML section. I also had trouble with the lab setup..."
"The support forum was not very responsive when I sought help with labs."
May be too basic for experienced practitioners seeking advanced topics.
"I was expecting more advanced techniques for specific use cases, it only covers the basics well."
"This course was too basic for me as an experienced practitioner; the depth for advanced feature construction or complex data types is missing."
"Some parts felt a bit basic if I already have some ML background, though it was good for a refresher."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Feature Engineering with these
activities:
Practice SQL queries to prepare for the course
Show steps
This course requires proficiency with SQL queries, so practice writing and executing queries to refresh and improve your skills.
Browse courses on
Structured Query Language
Show steps
-
Review SQL syntax and concepts
-
Create a database and populate it with data
-
Write queries to retrieve data from the database
Organize and review lecture notes, assignments, and quizzes
Show steps
Reviewing and organizing course materials will reinforce your understanding of the concepts covered and enhance your overall learning experience.
Show steps
-
Gather and organize lecture notes, assignments, and quizzes
-
Review and summarize key concepts
-
Identify areas where further clarification is needed
Follow tutorials on using Vertex AI Feature Store
Show steps
Enrolling in guided tutorials and workshops can provide step-by-step instructions and practical examples to reinforce your understanding of Vertex AI Feature Store concepts.
Browse courses on
Vertex AI Feature Store
Show steps
-
Identify and enroll in relevant tutorials
-
Follow the instructions and complete the exercises
-
Review the provided materials and examples
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join a study group to discuss course concepts
Show steps
Engaging with peers through discussion and collaboration can enhance your understanding of the course material and provide different perspectives.
Show steps
-
Find or form a study group with other students
-
Establish meeting times and schedules
-
Discuss course concepts and share insights
Solve coding challenges on feature engineering
Show steps
Engaging in coding challenges specifically focused on feature engineering will strengthen your ability to apply the techniques you learn in the course to real-world scenarios.
Browse courses on
Feature Engineering
Show steps
-
Identify coding challenge websites or platforms
-
Select challenges related to feature engineering
-
Attempt to solve the challenges on your own
Attend workshops on advanced feature engineering techniques
Show steps
Participating in workshops led by experts in the field can expose you to cutting-edge techniques and best practices in feature engineering, expanding your knowledge and skills.
Browse courses on
Feature Engineering
Show steps
-
Identify and register for relevant workshops
-
Attend the workshops and actively participate in discussions
-
Apply the learned techniques in your own projects
Develop a feature engineering pipeline for a specific dataset
Show steps
Building a feature engineering pipeline for a specific dataset will allow you to apply the concepts learned in the course to a practical project, solidifying your understanding and skills.
Browse courses on
Feature Engineering
Show steps
-
Choose a dataset and define the problem statement
-
Explore the data and identify potential features
-
Design and implement a feature engineering pipeline
Practice SQL queries to prepare for the course
Show steps
This course requires proficiency with SQL queries, so practice writing and executing queries to refresh and improve your skills.
Browse courses on
Structured Query Language
Show steps
Show steps
Show steps
- Review SQL syntax and concepts
- Create a database and populate it with data
- Write queries to retrieve data from the database
Organize and review lecture notes, assignments, and quizzes
Show steps
Reviewing and organizing course materials will reinforce your understanding of the concepts covered and enhance your overall learning experience.
Show steps
Show steps
Show steps
- Gather and organize lecture notes, assignments, and quizzes
- Review and summarize key concepts
- Identify areas where further clarification is needed
Follow tutorials on using Vertex AI Feature Store
Show steps
Enrolling in guided tutorials and workshops can provide step-by-step instructions and practical examples to reinforce your understanding of Vertex AI Feature Store concepts.
Browse courses on
Vertex AI Feature Store
Show steps
Show steps
Show steps
- Identify and enroll in relevant tutorials
- Follow the instructions and complete the exercises
- Review the provided materials and examples
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join a study group to discuss course concepts
Show steps
Engaging with peers through discussion and collaboration can enhance your understanding of the course material and provide different perspectives.
Show steps
Show steps
Show steps
- Find or form a study group with other students
- Establish meeting times and schedules
- Discuss course concepts and share insights
Solve coding challenges on feature engineering
Show steps
Engaging in coding challenges specifically focused on feature engineering will strengthen your ability to apply the techniques you learn in the course to real-world scenarios.
Browse courses on
Feature Engineering
Show steps
Show steps
Show steps
- Identify coding challenge websites or platforms
- Select challenges related to feature engineering
- Attempt to solve the challenges on your own
Attend workshops on advanced feature engineering techniques
Show steps
Participating in workshops led by experts in the field can expose you to cutting-edge techniques and best practices in feature engineering, expanding your knowledge and skills.
Browse courses on
Feature Engineering
Show steps
Show steps
Show steps
- Identify and register for relevant workshops
- Attend the workshops and actively participate in discussions
- Apply the learned techniques in your own projects
Develop a feature engineering pipeline for a specific dataset
Show steps
Building a feature engineering pipeline for a specific dataset will allow you to apply the concepts learned in the course to a practical project, solidifying your understanding and skills.
Browse courses on
Feature Engineering
Show steps
Show steps
Show steps
- Choose a dataset and define the problem statement
- Explore the data and identify potential features
- Design and implement a feature engineering pipeline
Career center
Learners who complete Feature Engineering will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists leverage large datasets to create valuable business insights through modeling, statistical analysis, and machine learning. This course may be helpful for aspiring Data Scientists by providing them with the skills to identify and extract useful features from raw data using Vertex AI Feature Store, BigQuery ML, TensorFlow, and other tools. The focus on feature engineering and model accuracy enhancement makes this course a valuable resource for individuals looking to build a strong foundation in data science.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models to solve complex business problems. This course may be helpful for Machine Learning Engineers by providing them with the knowledge and hands-on experience to improve the accuracy of ML models through effective feature engineering techniques. The course covers topics such as feature preprocessing, feature creation, feature crosses, and TensorFlow Transform, which are essential for building high-performing ML models.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make informed decisions. This course may be helpful for Data Analysts by providing them with the skills to prepare raw data for analysis and extract meaningful insights through feature engineering. The course covers topics such as data preprocessing, feature selection, and feature transformation, which are essential for building robust and interpretable data analysis models.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course may be helpful for Software Engineers by providing them with the knowledge and skills to incorporate feature engineering techniques into their software development process. The course covers topics such as data preprocessing, feature selection, and feature transformation, which are essential for building high-quality software applications that can handle complex data.
Business Analyst
Business Analyst
Business Analysts analyze business processes and data to identify areas for improvement. This course may be helpful for Business Analysts by providing them with the skills to extract meaningful insights from data through feature engineering. The course covers topics such as data preprocessing, feature selection, and feature transformation, which are essential for building data-driven business analysis models.
Product Manager
Product Manager
Product Managers define and manage the development of new products and features. This course may be helpful for Product Managers by providing them with the knowledge and skills to identify and prioritize features that meet customer needs and drive business value. The course covers topics such as feature engineering, feature prioritization, and data-driven decision-making, which are essential for building successful products.
Data Engineer
Data Engineer
Data Engineers design and build data pipelines and infrastructure. This course may be helpful for Data Engineers by providing them with the knowledge and skills to prepare and transform data for feature engineering. The course covers topics such as data preprocessing, data cleaning, and data integration, which are essential for building robust and scalable data pipelines.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to provide insights and make predictions. This course may be helpful for Statisticians by providing them with the skills to extract meaningful features from data and build statistical models for prediction and inference. The course covers topics such as feature engineering, statistical modeling, and data visualization, which are essential for building robust and interpretable statistical models.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course may be helpful for Quantitative Analysts by providing them with the skills to extract meaningful features from financial data and build predictive models for risk assessment and portfolio optimization. The course covers topics such as feature engineering, financial modeling, and risk management, which are essential for building robust and profitable quantitative investment strategies.
Researcher
Researcher
Researchers conduct scientific investigations to advance knowledge and understanding. This course may be helpful for Researchers by providing them with the skills to extract meaningful features from data and build models for hypothesis testing and theory development. The course covers topics such as feature engineering, data analysis, and scientific modeling, which are essential for building robust and reproducible research findings.
Consultant
Consultant
Consultants provide expert advice and guidance to businesses on a variety of topics. This course may be helpful for Consultants by providing them with the skills to extract meaningful features from data and build models for problem-solving and decision-making. The course covers topics such as feature engineering, data analysis, and business intelligence, which are essential for building robust and actionable consulting recommendations.
Project Manager
Project Manager
Project Managers plan, execute, and close projects. This course may be helpful for Project Managers by providing them with the skills to identify and prioritize features for project success. The course covers topics such as feature engineering, project management, and stakeholder management, which are essential for building successful projects.
Marketing Manager
Marketing Manager
Marketing Managers plan and execute marketing campaigns to promote products and services. This course may be helpful for Marketing Managers by providing them with the skills to identify and target customer segments based on their features. The course covers topics such as feature engineering, market segmentation, and campaign management, which are essential for building successful marketing campaigns.
Sales Manager
Sales Manager
Sales Managers lead and motivate sales teams to achieve revenue targets. This course may be helpful for Sales Managers by providing them with the skills to identify and prioritize customer needs based on their features. The course covers topics such as feature engineering, customer relationship management, and sales forecasting, which are essential for building successful sales teams.
Operations Manager
Operations Manager
Operations Managers plan and execute operations to achieve business goals. This course may be helpful for Operations Managers by providing them with the skills to identify and prioritize operational improvements based on data. The course covers topics such as feature engineering, process improvement, and data-driven decision-making, which are essential for building efficient and effective operations.
For more career information including salaries, visit:
OpenCourser.com/course/fs21lb/feature
Reading list
We've selected 13 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Feature Engineering.
An in-depth guide to feature engineering principles and techniques with practical examples in Python.
A hands-on guide to feature engineering using Python, covering data preprocessing, feature selection, and feature transformation.
A practical guide to feature engineering and selection for predictive modeling, providing a comprehensive overview of the field.
Save
A comprehensive textbook on predictive modeling, covering feature engineering, model selection, and evaluation.
A classic textbook on statistical learning, providing a mathematical foundation for feature engineering.
A comprehensive textbook on pattern recognition and machine learning, covering feature engineering as part of the model building process.
Save
A comprehensive textbook on data mining, providing a practical overview of feature engineering.
Save
A classic textbook on machine learning, providing a theoretical foundation for feature engineering.
A non-technical introduction to machine learning, providing a basic understanding of feature engineering.
A concise introduction to machine learning, providing a brief overview of feature engineering.
A very basic introduction to machine learning, providing a gentle introduction to feature engineering.
A comprehensive guide to machine learning using Python, covering feature engineering as part of the data preprocessing process.
A practical guide to machine learning using popular Python libraries, covering feature engineering as part of the model building process.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/fs21lb/feature
Share
Similar courses
Similar courses are unavailable at this time. Please try again later.
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2025 OpenCourser