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Google Cloud Training
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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What's inside
Syllabus
Module 0: Introduction
This module provides an overview of the course and its objectives.
Module 1: Introduction to Vertex AI Feature Store
This module introduces Vertex AI Feature Store.
Read more
Syllabus
Module 0: Introduction
This module provides an overview of the course and its objectives.
Module 1: Introduction to Vertex AI Feature Store
This module introduces Vertex AI Feature Store.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Teaches methods of feature engineering, a standard process for improving machine learning models
The instructors, Google Cloud Training, are experts in Google's cloud-based machine learning platforms, including Vertex AI
Provides hands-on labs for practicing concepts and testing models
Covers advanced techniques including feature crosses and TensorFlow Transform for feature engineering
Introduces industry-standard techniques used by professional data engineers in building machine learning models
Requires familiarity with Python and basic machine learning concepts
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Reviews summary
Practical introduction to ml feature engineering
According to learners, this course offers a solid foundation in feature engineering, particularly within the Google Cloud ecosystem. Students appreciate the practical labs using tools like BigQuery ML, TensorFlow, and Vertex AI Feature Store. Many found the course well-structured and the introduction to key concepts clear. However, some mention that certain advanced topics or specific tools like Dataflow and Apache Beam felt rushed or complex, suggesting it might be less suitable for complete beginners or those without prior GCP experience. Overall, it's seen as a valuable resource for applying feature engineering techniques with Google Cloud.
Pace can be fast, depth varies by topic.
"Some sections felt a bit rushed, especially the Dataflow part."
"Could use more in-depth coverage on complex topics or optimization techniques."
"While the basics are covered well, advanced topics could be explored further."
Introduces Vertex AI, BigQuery ML, TF Transform.
"The course introduces key tools like Vertex AI Feature Store and BigQuery ML."
"I learned about the use cases for TensorFlow Transform."
"The content on Vertex AI Feature Store was particularly relevant for my work."
Provides a strong basis in feature engineering.
"Provides a solid introduction to feature engineering concepts."
"The course provides a good overview of what makes a good feature and representations."
"This course gave me a strong foundation in feature engineering principles."
Hands-on labs using Google Cloud tools.
"The labs using BigQuery ML and Vertex AI Feature Store were incredibly useful and hands-on."
"I really liked the practical exercises that allowed me to apply the concepts learned."
"The hands-on coding and projects are the strongest part of the course for me."
Best for those with ML/GCP background.
"It helps to have some prior experience with Google Cloud Platform."
"Not for absolute beginners in both ML and cloud computing."
"Learners should ideally have a basic understanding of machine learning concepts before starting."
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:
Review basic data manipulation
Show steps
Review basic data manipulation and cleaning techniques to ensure a solid foundation for feature engineering.
Browse courses on
Data Cleaning
Show steps
-
Review data normalization techniques
-
Practice cleaning and filtering data using Python or R
Build a simple machine learning model using a dataset
Show steps
Apply feature engineering techniques to improve the accuracy of a machine learning model.
Browse courses on
Machine Learning Models
Show steps
-
Choose a dataset and define a machine learning task
-
Apply feature engineering techniques to the dataset
-
Train and evaluate the machine learning model
Explore use cases of Vertex AI Feature Store
Show steps
Deepen understanding of Vertex AI Feature Store and its applications in real-world scenarios.
Browse courses on
Vertex AI
Show steps
-
Follow online tutorials and documentation to learn about Vertex AI Feature Store
-
Explore case studies and examples of using Feature Store in practice
Two other activities
Expand to see all activities and additional details
Show all five activities
Read 'Feature Engineering for Machine Learning' by Alice Zheng
Show steps
Expand knowledge of feature engineering principles and techniques through reading a recommended book.
View
Feature Engineering for Machine Learning:...
on Amazon
Show steps
-
Read selected chapters or sections from the book
-
Take notes and summarize key concepts
Solve feature engineering practice problems
Show steps
Reinforce feature engineering skills by solving practice problems.
Browse courses on
Feature Engineering
Show steps
-
Find practice problems online or in textbooks
-
Attempt to solve the problems independently
-
Review solutions and learn from mistakes
Review basic data manipulation
Show steps
Review basic data manipulation and cleaning techniques to ensure a solid foundation for feature engineering.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Review data normalization techniques
- Practice cleaning and filtering data using Python or R
Build a simple machine learning model using a dataset
Show steps
Apply feature engineering techniques to improve the accuracy of a machine learning model.
Browse courses on
Machine Learning Models
Show steps
Show steps
Show steps
- Choose a dataset and define a machine learning task
- Apply feature engineering techniques to the dataset
- Train and evaluate the machine learning model
Explore use cases of Vertex AI Feature Store
Show steps
Deepen understanding of Vertex AI Feature Store and its applications in real-world scenarios.
Browse courses on
Vertex AI
Show steps
Show steps
Show steps
- Follow online tutorials and documentation to learn about Vertex AI Feature Store
- Explore case studies and examples of using Feature Store in practice
Two other activities
Expand to see all activities and additional details
Show all five activities
Read 'Feature Engineering for Machine Learning' by Alice Zheng
Show steps
Expand knowledge of feature engineering principles and techniques through reading a recommended book.
View
Feature Engineering for Machine Learning:...
on Amazon
Show steps
Show steps
Show steps
- Read selected chapters or sections from the book
- Take notes and summarize key concepts
Solve feature engineering practice problems
Show steps
Reinforce feature engineering skills by solving practice problems.
Browse courses on
Feature Engineering
Show steps
Show steps
Show steps
- Find practice problems online or in textbooks
- Attempt to solve the problems independently
- Review solutions and learn from mistakes
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 are experts in using data to solve business problems. They collect, clean, and analyze data to identify trends and patterns. They then use this information to build predictive models and make recommendations to businesses. A course on feature engineering can help Data Scientists improve the accuracy of their models and find which data columns make the most useful features. This can lead to better decision-making and improved business outcomes.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for building and deploying machine learning models. They work with Data Scientists to identify the right data and features to use, and then they develop and train the models. A course on feature engineering can help Machine Learning Engineers build more accurate and efficient models. This can lead to better business outcomes and a competitive advantage.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data. They use this information to identify trends and patterns, and then they report their findings to businesses. A course on feature engineering can help Data Analysts improve the quality of their data and make more accurate predictions. This can lead to better decision-making and improved business outcomes.
Business Analyst
Business Analyst
Business Analysts are responsible for understanding the needs of a business and then developing solutions to meet those needs. They work with stakeholders to gather requirements, analyze data, and make recommendations. A course on feature engineering can help Business Analysts better understand the data that is available to them and make more informed decisions. This can lead to better solutions and improved business outcomes.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software applications. They work with stakeholders to gather requirements, design the application architecture, and write the code. A course on feature engineering can help Software Engineers build more efficient and effective applications. This can lead to better user experiences and improved business outcomes.
Product Manager
Product Manager
Product Managers are responsible for the overall success of a product. They work with stakeholders to define the product vision, develop the product roadmap, and launch the product to market. A course on feature engineering can help Product Managers better understand the technical aspects of product development and make more informed decisions. This can lead to better products and improved business outcomes.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts are responsible for using mathematical and statistical models to analyze data and make predictions. They work with stakeholders to identify the right data and features to use, and then they develop and validate the models. A course on feature engineering can help Quantitative Analysts build more accurate and efficient models. This can lead to better decision-making and improved business outcomes.
Statistician
Statistician
Statisticians are responsible for collecting, analyzing, and interpreting data. They use this information to make inferences about the world around us. A course on feature engineering can help Statisticians improve the quality of their data and make more accurate predictions. This can lead to better decision-making and improved business outcomes.
Data Engineer
Data Engineer
Data Engineers are responsible for building and maintaining the infrastructure that is used to store and process data. They work with Data Scientists and Machine Learning Engineers to ensure that the data is available and accessible. A course on feature engineering can help Data Engineers build more efficient and effective data pipelines. This can lead to better data quality and improved business outcomes.
Database Administrator
Database Administrator
Database Administrators are responsible for managing and maintaining databases. They ensure that the data is secure and accessible to users. A course on feature engineering can help Database Administrators better understand the data that is stored in their databases and make more informed decisions about how to manage it. This can lead to improved data quality and improved business outcomes.
Information Architect
Information Architect
Information Architects are responsible for designing and managing the information architecture of an organization. They work with stakeholders to understand the organization's information needs and then develop a plan for how to meet those needs. A course on feature engineering can help Information Architects better understand the data that is available to them and how to make it more accessible to users. This can lead to improved decision-making and improved business outcomes.
Data Governance Analyst
Data Governance Analyst
Data Governance Analysts are responsible for developing and implementing data governance policies and procedures. They work with stakeholders to ensure that the data is used in a consistent and ethical manner. A course on feature engineering can help Data Governance Analysts better understand the data that is available to them and how to make it more accessible to users. This can lead to improved data quality and improved business outcomes.
Data Privacy Analyst
Data Privacy Analyst
Data Privacy Analysts are responsible for ensuring that an organization's data is used in a compliant manner. They work with stakeholders to identify and mitigate data privacy risks. A course on feature engineering can help Data Privacy Analysts better understand the data that is available to them and how to make it more secure. This can lead to improved data privacy and improved business outcomes.
Information Security Analyst
Information Security Analyst
Information Security Analysts are responsible for protecting an organization's data from unauthorized access. They work with stakeholders to identify and mitigate security risks. A course on feature engineering can help Information Security Analysts better understand the data that is available to them and how to make it more secure. This can lead to improved data security and improved business outcomes.
Software Architect
Software Architect
Software Architects are responsible for designing and developing the overall architecture of a software application. They work with stakeholders to understand the application's requirements and then design the system that will meet those requirements. A course on feature engineering can help Software Architects better understand the data that is needed by the application and how to make it available to the application's users. This can lead to better system performance and improved business outcomes.
For more career information including salaries, visit:
OpenCourser.com/course/f1gly3/feature
Reading list
We've selected ten 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.
Classic textbook on statistical learning. It provides a comprehensive overview of the field, including chapters on feature engineering, model selection, and hyperparameter tuning. It is written by leading researchers in the field of statistical learning.
Textbook on statistical learning. It provides a comprehensive overview of the field, including chapters on feature engineering, model selection, and hyperparameter tuning. It is written by leading researchers in the field of statistical learning.
Provides a comprehensive overview of feature engineering for deep learning. It covers the principles and best practices of feature engineering, as well as practical examples and case studies. It is written by leading researchers in the field of deep learning.
Provides a practical guide to feature engineering with Python. It covers a wide range of topics, including data preprocessing, feature selection, and feature transformation. It also includes hands-on exercises and examples.
Covers feature engineering for text data. It provides a comprehensive overview of the topic, including techniques for text preprocessing, text feature extraction, and text feature selection. It also includes hands-on examples and exercises.
Provides a practical guide to machine learning with Python. It covers a wide range of topics, including feature engineering, model selection, and hyperparameter tuning. It also includes hands-on exercises and examples.
Save
Provides a comprehensive overview of data mining and machine learning. It covers a wide range of topics, including feature engineering, model selection, and hyperparameter tuning. It also includes hands-on exercises and examples.
Introduces the fundamentals of feature engineering for machine learning, including techniques such as data preprocessing, dimensionality reduction, and feature selection. It covers the principles and best practices of feature engineering, as well as practical examples and case studies.
Provides a practical guide to machine learning for hackers. It covers a wide range of topics, including feature engineering, model selection, and hyperparameter tuning. It also includes hands-on exercises and examples.
Provides a comprehensive overview of machine learning with R. It covers a wide range of topics, including feature engineering, model selection, and hyperparameter tuning. It also includes hands-on exercises and examples.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/f1gly3/feature
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Effort
2 weeks of study, 5-7 hours per week
Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
This course belongs to a
collection
called
Machine Learning on Google Cloud. It's comprised of seven courses:
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