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Google Cloud
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
Save this course
Save How Google Does Machine Learning to your list so you can find it easily later:
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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 How Google Does Machine Learning with these
activities:
Watch tutorials from industry experts
Show steps
Exposes you to different perspectives and best practices.
Show steps
-
Identify reputable sources for tutorials.
-
Choose a topic that you want to learn more about.
-
Watch the tutorial.
-
Take notes and ask questions.
Create a knowledge repository
Show steps
Improve your retention and organization by compiling a comprehensive set of resources related to ML.
Browse courses on
Note-Taking
Show steps
-
Gather materials from the course, including lecture notes, assignments, and additional resources
-
Create a structured system for organizing and categorizing the materials
-
Regularly review and update your repository to ensure it remains relevant and comprehensive
-
Share your repository with others to contribute to the broader ML community
Review prerequisite topics
Show steps
Improve your comprehension of fundamental concepts to strengthen your foundation for this course.
Browse courses on
Machine Learning Basics
Show steps
-
Review materials on supervised and unsupervised learning
-
Go over basic algorithms like linear regression and decision trees
-
Brush up on data preprocessing techniques
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Create a machine learning model diagram
Show steps
Helps you understand the components and flow of a machine learning model.
Browse courses on
Machine Learning Models
Show steps
-
Identify the input and output data.
-
Choose a machine learning algorithm.
-
Train the model.
-
Evaluate the model.
-
Deploy the model.
Engage in study groups
Show steps
Enhance your understanding through collaborative learning and sharing perspectives with peers.
Browse courses on
Collaboration
Show steps
-
Form study groups with classmates or join existing ones
-
Meet regularly to discuss course material, share insights, and work on assignments together
-
Provide feedback and support to each other to improve comprehension
Review industry best practices for machine learning
Show steps
Become familiar with common pitfalls and proven techniques in the field of machine learning, which will inform your approach to the projects you build in this course.
Show steps
-
Identify reputable sources for machine learning best practices.
-
Read articles, blog posts, and whitepapers on the topic.
-
Attend webinars and online events featuring experts in the field.
-
Experiment with different best practices to see what works best for your projects.
Complete coding exercises
Show steps
Enhance your practical skills and reinforce your understanding of ML implementation.
Browse courses on
Python
Show steps
-
Solve coding challenges on platforms like LeetCode or HackerRank
-
Work on hands-on projects and experiment with different ML techniques
-
Participate in online coding competitions or hackathons
Practice building and training machine learning models
Show steps
Build a strong foundation in the practical aspects of machine learning by getting hands-on experience with data preparation, model selection, and evaluation techniques.
Show steps
-
Choose a dataset and define a machine learning problem.
-
Preprocess and explore the data.
-
Select and train several different machine learning models.
-
Evaluate the performance of the models and select the best one.
-
Deploy the model and monitor its performance over time.
Solve machine learning coding challenges
Show steps
Improves your understanding of ML algorithms and their implementation.
Browse courses on
Machine Learning Algorithms
Show steps
-
Find a coding challenge platform.
-
Choose a challenge that matches your skill level.
-
Solve the challenge.
-
Review your solution and identify areas for improvement.
Explore external resources
Show steps
Expand your knowledge by exploring additional learning materials and engaging with the ML community.
Browse courses on
Coursera
Show steps
-
Enroll in online courses or workshops to gain specialized insights
-
Follow industry blogs and attend webinars to stay updated on trends
-
Join online forums and discussion groups to connect with ML practitioners
Participate in online competitions
Show steps
Challenge yourself and gain practical experience by participating in real-world ML challenges.
Browse courses on
Kaggle Competitions
Show steps
-
Identify relevant competitions or hackathons that align with your interests
-
Team up with others or work individually to develop ML solutions
-
Submit your results and receive feedback from experts and the community
-
Reflect on your performance and identify areas for improvement
Develop a personal ML project
Show steps
Deepen your practical skills and showcase your abilities by creating a project that applies ML concepts.
Browse courses on
Project-Based Learning
Show steps
-
Identify a real-world problem or opportunity that can be addressed with ML
-
Gather and prepare data, selecting appropriate features and techniques
-
Train and evaluate different ML models to find the best solution
-
Deploy and monitor the ML model, tracking its performance and making adjustments as needed
-
Document your project and share your findings in a report or presentation
Build a machine learning portfolio project
Show steps
Provides hands-on experience and showcases your skills.
Browse courses on
Machine Learning Projects
Show steps
-
Identify a problem that can be solved with machine learning.
-
Collect and prepare data.
-
Develop and train a machine learning model.
-
Evaluate and deploy the model.
-
Document your project and share it with others.
Help other students
Show steps
Enhance your own understanding while supporting others on their learning journey.
Browse courses on
Mentorship
Show steps
-
Join online forums or discussion groups related to ML
-
Offer assistance to learners with questions or challenges
-
Provide constructive feedback and guidance, helping others refine their ML skills
-
Share your own experiences and insights to benefit the wider ML community
Watch tutorials from industry experts
Show steps
Exposes you to different perspectives and best practices.
Show steps
Show steps
Show steps
- Identify reputable sources for tutorials.
- Choose a topic that you want to learn more about.
- Watch the tutorial.
- Take notes and ask questions.
Create a knowledge repository
Show steps
Improve your retention and organization by compiling a comprehensive set of resources related to ML.
Browse courses on
Note-Taking
Show steps
Show steps
Show steps
- Gather materials from the course, including lecture notes, assignments, and additional resources
- Create a structured system for organizing and categorizing the materials
- Regularly review and update your repository to ensure it remains relevant and comprehensive
- Share your repository with others to contribute to the broader ML community
Review prerequisite topics
Show steps
Improve your comprehension of fundamental concepts to strengthen your foundation for this course.
Browse courses on
Machine Learning Basics
Show steps
Show steps
Show steps
- Review materials on supervised and unsupervised learning
- Go over basic algorithms like linear regression and decision trees
- Brush up on data preprocessing techniques
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Create a machine learning model diagram
Show steps
Helps you understand the components and flow of a machine learning model.
Browse courses on
Machine Learning Models
Show steps
Show steps
Show steps
- Identify the input and output data.
- Choose a machine learning algorithm.
- Train the model.
- Evaluate the model.
- Deploy the model.
Engage in study groups
Show steps
Enhance your understanding through collaborative learning and sharing perspectives with peers.
Browse courses on
Collaboration
Show steps
Show steps
Show steps
- Form study groups with classmates or join existing ones
- Meet regularly to discuss course material, share insights, and work on assignments together
- Provide feedback and support to each other to improve comprehension
Review industry best practices for machine learning
Show steps
Become familiar with common pitfalls and proven techniques in the field of machine learning, which will inform your approach to the projects you build in this course.
Show steps
Show steps
Show steps
- Identify reputable sources for machine learning best practices.
- Read articles, blog posts, and whitepapers on the topic.
- Attend webinars and online events featuring experts in the field.
- Experiment with different best practices to see what works best for your projects.
Complete coding exercises
Show steps
Enhance your practical skills and reinforce your understanding of ML implementation.
Browse courses on
Python
Show steps
Show steps
Show steps
- Solve coding challenges on platforms like LeetCode or HackerRank
- Work on hands-on projects and experiment with different ML techniques
- Participate in online coding competitions or hackathons
Practice building and training machine learning models
Show steps
Build a strong foundation in the practical aspects of machine learning by getting hands-on experience with data preparation, model selection, and evaluation techniques.
Show steps
Show steps
Show steps
- Choose a dataset and define a machine learning problem.
- Preprocess and explore the data.
- Select and train several different machine learning models.
- Evaluate the performance of the models and select the best one.
- Deploy the model and monitor its performance over time.
Solve machine learning coding challenges
Show steps
Improves your understanding of ML algorithms and their implementation.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Find a coding challenge platform.
- Choose a challenge that matches your skill level.
- Solve the challenge.
- Review your solution and identify areas for improvement.
Explore external resources
Show steps
Expand your knowledge by exploring additional learning materials and engaging with the ML community.
Browse courses on
Coursera
Show steps
Show steps
Show steps
- Enroll in online courses or workshops to gain specialized insights
- Follow industry blogs and attend webinars to stay updated on trends
- Join online forums and discussion groups to connect with ML practitioners
Participate in online competitions
Show steps
Challenge yourself and gain practical experience by participating in real-world ML challenges.
Browse courses on
Kaggle Competitions
Show steps
Show steps
Show steps
- Identify relevant competitions or hackathons that align with your interests
- Team up with others or work individually to develop ML solutions
- Submit your results and receive feedback from experts and the community
- Reflect on your performance and identify areas for improvement
Develop a personal ML project
Show steps
Deepen your practical skills and showcase your abilities by creating a project that applies ML concepts.
Browse courses on
Project-Based Learning
Show steps
Show steps
Show steps
- Identify a real-world problem or opportunity that can be addressed with ML
- Gather and prepare data, selecting appropriate features and techniques
- Train and evaluate different ML models to find the best solution
- Deploy and monitor the ML model, tracking its performance and making adjustments as needed
- Document your project and share your findings in a report or presentation
Build a machine learning portfolio project
Show steps
Provides hands-on experience and showcases your skills.
Browse courses on
Machine Learning Projects
Show steps
Show steps
Show steps
- Identify a problem that can be solved with machine learning.
- Collect and prepare data.
- Develop and train a machine learning model.
- Evaluate and deploy the model.
- Document your project and share it with others.
Help other students
Show steps
Enhance your own understanding while supporting others on their learning journey.
Browse courses on
Mentorship
Show steps
Show steps
Show steps
- Join online forums or discussion groups related to ML
- Offer assistance to learners with questions or challenges
- Provide constructive feedback and guidance, helping others refine their ML skills
- Share your own experiences and insights to benefit the wider ML community
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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