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How Google Does Machine Learning
Google Cloud
Google Cloud
This course explores what ML is and what problems it can solve.
Read more
This course explores what ML is and what problems it can solve.
Read more
This course explores what ML is and what problems it can solve.
This course explores what ML is and what problems it can solve. The course also discusses best practices for implementing machine learning. You’re introduced to Vertex AI, a unified platform to quickly build, train, and deploy AutoML machine learning models. The course discusses the five phases of converting a candidate use case to be driven by machine learning, and why it’s important to not skip them. The course ends with recognizing the biases that ML can amplify and how to recognize them.
Register for this course and see more details by visiting:
OpenCourser.com/course/oq9e8v/how
What's inside
Syllabus
Introduction to Course and Series
What It Means to be AI-First
How Google Does ML
Machine Learning Development with Vertex AI
Read more
Syllabus
Introduction to Course and Series
What It Means to be AI-First
How Google Does ML
Machine Learning Development with Vertex AI
Read more
Machine Learning Development with Vertex Notebooks
Best Practices for Implementing Machine Learning on Vertex AI
Responsible AI Development
Summary
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by Google Cloud, who are recognized for their work in AI development
Develops skills and knowledge that are highly relevant in an academic setting
Explores what ML is and what problems it can solve, which is standard in industry
Teaches best practices for implementing machine learning, which helps learners improve their models
Provides a comprehensive study of AI development, including ML
Introduces Vertex AI, a unified platform for building, training, and deploying ML models, which is a useful tool
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Activities
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How Google Does Machine Learning.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete How Google Does Machine Learning will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
For professionals wanting to become Machine Learning Engineers, this course will help build a foundation in the roles and responsibilities of this highly sought-after ML Engineer. The discussion of best practices for implementing Machine Learning on Vertex AI will be critical to success in this field. While this course touches on the fundamentals of Machine Learning, related courses from the same provider can provide further depth of knowledge.
Data Analyst
Data Analyst
To become a highly effective Data Analyst, this course can provide a solid introduction to Machine Learning. The discussion of best practices for implementing Machine Learning on Vertex AI will be critical to success in this field. The course may also be helpful for Data Analysts who want to increase their knowledge of Machine Learning and its real-world applications.
Data Scientist
Data Scientist
For individuals who want to work as Data Scientists, this course can provide a great introduction to the field, with a specific focus on best practices for implementing Machine Learning on Vertex AI. The course may also be helpful for Data Scientists who want to increase their knowledge of Machine Learning and its real-world applications.
Software Engineer
Software Engineer
This course may be useful for Software Engineers who want to incorporate Machine Learning into their work, particularly in the area of implementing ML on Vertex AI. The course may also be helpful for Software Engineers who want to learn more about the broader field of Machine Learning and its potential applications.
Business Analyst
Business Analyst
For Business Analysts, this course can provide a solid overview of Machine Learning, particularly in the context of best practices for implementing Machine Learning on Vertex AI. The course may also be helpful for Business Analysts who want to learn more about the broader field of Machine Learning and its potential applications for business.
Product Manager
Product Manager
This course may be useful for Product Managers who want to incorporate Machine Learning into their work. The course can provide a solid overview of the field, and the discussion of best practices for implementing Machine Learning on Vertex AI will be critical to success in this field.
Quantitative Analyst
Quantitative Analyst
For those interested in becoming Quantitative Analysts, this course can provide a great foundation in the field, with a specific focus on best practices for implementing Machine Learning on Vertex AI. The course may also be helpful for Quantitative Analysts who want to increase their knowledge of Machine Learning and its real-world applications.
Data Engineer
Data Engineer
This course may be useful for Data Engineers, particularly those who work with Machine Learning. The course can provide a solid overview of the field, and the discussion of best practices for implementing Machine Learning on Vertex AI will be critical to success in this field.
Machine Learning Architect
Machine Learning Architect
For professionals interested in advancing their career to become Machine Learning Architects, this course may be useful. The course can provide a solid overview of the field, and the discussion of best practices for implementing Machine Learning on Vertex AI will be critical to success in this field.
Machine Learning Researcher
Machine Learning Researcher
For those interested in working as Machine Learning Researchers, this course may be useful. The course can provide a solid overview of the field, and the discussion of best practices for implementing Machine Learning on Vertex AI will be critical to success in this field.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
For individuals interested in advancing their career to become Artificial Intelligence Engineers, this course may be useful. The course can provide a solid overview of the field, and the discussion of best practices for implementing Machine Learning on Vertex AI will be critical to success in this field.
Computer Vision Engineer
Computer Vision Engineer
This course may be useful for Computer Vision Engineers, particularly those who work on projects that involve Machine Learning. The course can provide a solid overview of the field, and the discussion of best practices for implementing Machine Learning on Vertex AI will be critical to success in this field.
Natural Language Processing Engineer
Natural Language Processing Engineer
This course may be useful for Natural Language Processing Engineers, particularly those who work with Machine Learning. The course can provide a solid overview of the field, and the discussion of best practices for implementing Machine Learning on Vertex AI will be critical to success in this field.
Robotics Engineer
Robotics Engineer
For Robotics Engineers, this course may be useful for those who want to incorporate Machine Learning into their work. The course can provide a solid overview of the field, and the discussion of best practices for implementing Machine Learning on Vertex AI will be critical to success in this field.
Operations Research Analyst
Operations Research Analyst
This course may be useful for Operations Research Analysts, particularly those who work with Machine Learning. The course can provide a solid overview of the field, and the discussion of best practices for implementing Machine Learning on Vertex AI will be critical to success in this field.
For more career information including salaries, visit:
OpenCourser.com/course/oq9e8v/how
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
How Google Does Machine Learning.
Save
Comprehensive guide to deep learning, covering both the theoretical foundations and practical applications. It valuable resource for anyone who wants to learn more about deep learning.
### **Fit Description**
practical guide to deep learning in Python. It covers all the major deep learning techniques, and it is written in a clear and concise style.
### **Fit Description**
comprehensive guide to pattern recognition and machine learning. It covers all the major pattern recognition and machine learning techniques, and it good choice for students who want to learn more about the underlying theory.
### **Fit Description**
comprehensive guide to statistical learning. It covers all the major statistical learning techniques, and it good choice for students who want to learn more about the underlying theory.
### **Fit Description**
practical guide to machine learning in Python. It covers all the major machine learning techniques, and it is written in a clear and concise style.
Save
### **Fit Description**
comprehensive guide to data mining. It covers all the major data mining techniques, and it good choice for students who want to learn more about the underlying theory.
Provides a probabilistic perspective on machine learning, covering both the theoretical foundations and practical applications. It valuable resource for anyone who wants to learn more about machine learning from a probabilistic perspective.
### **Fit Description**
comprehensive guide to statistical learning. It covers all the major statistical learning techniques, and it good choice for students who want to learn more about the underlying theory.
### **Fit Description**
practical guide to machine learning for hackers. It covers all the major machine learning techniques, and it is written in a fun and engaging style.
Provides a practical introduction to machine learning using Scikit-Learn, Keras, and TensorFlow. It valuable resource for anyone who wants to learn how to build and train machine learning models.
Provides a practical introduction to machine learning using R. It valuable resource for anyone who wants to learn how to build and train machine learning models in R.
Provides a practical introduction to machine learning using Python. It valuable resource for anyone who wants to learn how to build and train machine learning models in Python.
Provides a gentle introduction to machine learning for beginners. It valuable resource for anyone who wants to learn the basics of machine learning without getting bogged down in the technical details.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/oq9e8v/how
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Time
162 hours
Level
Introductory
Via
Pluralsight
Instructor
Google Cloud
Language
English
Good to know
Taught by Google Cloud, who are recognized for their work in AI development
Develops skills and knowledge that are highly relevant in an academic setting
Explores what ML is and what problems it can solve, which is standard in industry
Teaches best practices for implementing machine learning, which helps learners improve their models
Provides a comprehensive study of AI development, including ML
Introduces Vertex AI, a unified platform for building, training, and deploying ML models, which is a useful tool
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