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Google Cloud Training
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/efam6r/how
What's inside
Syllabus
Introduction to Course and Series
This module introduces the course series and the Google experts who will be teaching it.
What It Means to be AI-First
Read more
Syllabus
Introduction to Course and Series
This module introduces the course series and the Google experts who will be teaching it.
What It Means to be AI-First
Read more
In this module, you explore building a data strategy around machine learning.
How Google Does ML
This module shares the organizational know-how Google has acquired over the years.
Machine Learning Development with Vertex AI
All machine learning starts with some type of goal - whether it be a business use case, academic use case, or goal you are trying to solve. This module reviews the process of determining whether the model is ready for production the “proof of concept” or “experimentation” phase.
Machine Learning Development with Vertex Notebooks
This module explores both managed notebooks and user-managed notebooks for machine learning development in Vertex AI.
Best Practices for Implementing Machine Learning on Vertex AI
This module reviews best practices for a number of different machine learning processes in Vertex AI.
Responsible AI Development
This module discusses why machine learning systems aren’t fair by default and some of the things you have to keep in mind as you infuse ML into your products.
Summary
This module is a summary of the How Google Does Machine Learning course.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides a comprehensive overview of machine learning and its applications in various industries
Features instructors from Google Cloud, who are recognized experts in machine learning
Introduces Google Vertex AI, a platform for building, training, and deploying machine learning models
Explores the five phases of implementing machine learning use cases and emphasizes the importance of following them
Raises awareness about potential biases in machine learning systems and offers guidance on recognizing them
Requires learners to have a basic understanding of machine learning concepts
Save this course
Save How Google does Machine Learning to your list so you can find it easily later:
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Reviews summary
Google machine learning strategy
**Learners say** this Google Cloud Platform course begins with insightful introductions to the basic principles of machine learning (ML) and how Google implements ML in its products. Detailed explanations cover the concepts, processes, and benefits of ML with engaging examples. Hands-on labs offer further exploration of Google's ML tools and services. Students appreciate the real-world approach, but note that the course is more focused on strategy and overview than in-depth technical training.
Introduces Google's cloud computing platform.
"It's a great course to catch up to all the latest technologies and assets of Google in AI field as well as providing very useful insights about how to ML."
"Very good resources and good knowledge in the field of instructor, specially ms, sara robinson. I like her lectures."
"The course is packed with wonderful learning resources."
Features interactive labs and exercises.
"The Labs were interesting and the content also. Thanks."
"Great hands on labs."
"Could not be better, hands on practice is the best way of learning."
Discusses real-world use cases and impact.
"I loved how this course explained the practical aspects of patience and waiting for and collecting data."
"It was astonishing and broaden my perspective to machine learning and data science."
"I really learnt the things that people never thought of it and thanks good cloud for providing with this knowledge."
Shows how Google thinks about and applies ML.
"Unlike the other courses I have already taken which focus more on the implementation of machine learning. This course helps you think like a strategist when it comes to "How Google does Machine Learning"."
"This course is probably one of the finest curated introduction to an organizational ML solution standard."
May be too broad and not specific enough.
"Felt like I was watching a Google ad."
"I found the course more of a marketing for GCP..."
"It's not as expected. The teachers try to just roam around the topic instead of explaining the core concept."
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:
Review computer programming
Show steps
Revisit fundamental computer programming concepts such as variables, data structures, and control flow, which are essential for understanding machine learning algorithms and implementing them in code.
Browse courses on
Python
Show steps
-
Review online tutorials or books on basic programming.
-
Practice writing simple programs and solving coding exercises.
-
Focus on understanding the underlying concepts and their application in machine learning.
-
Complete online coding challenges to test your understanding.
Organize and review course materials
Show steps
Regularly review and organize lecture notes, assignments, and other course materials to improve understanding, retention, and preparation for assessments.
Show steps
-
Gather all course materials, including notes and resources.
-
Create a designated space for organizing materials.
-
Review materials periodically, both during and after class.
-
Highlight important concepts and make annotations.
Review Data Engineering Fundamentals
Show steps
Refresh your knowledge of core data engineering concepts such as data modeling, data integration, and data pipelines to enhance your understanding of machine learning principles and applications.
Browse courses on
Data Engineering
Show steps
-
Review key data engineering concepts from introductory materials or online resources.
-
Practice data modeling techniques using diagramming tools or online platforms.
-
Complete hands-on exercises to implement data integration processes.
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Curate a Collection of Machine Learning Resources
Show steps
Enhance your understanding and stay updated on the latest advancements in machine learning by compiling a collection of valuable resources, including online courses, tutorials, articles, and industry blogs.
Browse courses on
Online Courses
Show steps
-
Identify reputable sources of machine learning content, such as online platforms, academic institutions, and industry thought leaders.
-
Categorize and organize the resources based on topics, difficulty levels, and relevance to your learning objectives.
-
Regularly update your compilation with new and relevant resources to stay current with the field.
Follow online machine learning tutorials
Show steps
Explore and follow online tutorials that guide you through building and deploying machine learning models using popular libraries like TensorFlow and scikit-learn.
Browse courses on
Machine Learning Workflow
Show steps
-
Identify reputable sources for machine learning tutorials.
-
Choose tutorials that align with your learning goals.
-
Follow the tutorials step-by-step.
-
Implement the code examples and experiment with different parameters.
-
Refer to the tutorials for reference and troubleshooting support.
Solve Machine Learning Practice Problems
Show steps
Enhance your understanding of machine learning algorithms by solving practice problems and applying your knowledge to real-world scenarios.
Browse courses on
Machine Learning Algorithms
Show steps
-
Identify a reputable online platform or textbook with machine learning practice problems.
-
Choose a specific machine learning algorithm, such as linear regression or decision trees.
-
Solve practice problems related to the chosen algorithm, focusing on understanding the underlying concepts.
-
Analyze your solutions and identify areas for improvement.
Solve coding challenges
Show steps
Practice solving coding challenges on platforms like LeetCode and HackerRank to improve your understanding of machine learning algorithms and problem-solving skills.
Browse courses on
Machine Learning Algorithms
Show steps
-
Choose a coding challenge platform.
-
Select a machine learning-related challenge.
-
Solve the challenge using the techniques learned in the course.
-
Review your solution and identify areas for improvement.
-
Repeat with different challenges to improve your skills.
Connect with Mentors in the Machine Learning Field
Show steps
Accelerate your learning by seeking guidance from experienced professionals in the machine learning field who can provide personalized advice, support, and industry insights.
Browse courses on
Mentoring
Show steps
-
Identify potential mentors through industry events, online communities, or professional networks.
-
Reach out to potential mentors and express your interest in their guidance.
-
Establish clear expectations and goals for the mentoring relationship.
Attend a Machine Learning Workshop or Meetup
Show steps
Expand your knowledge and connect with experts in the field by attending a machine learning workshop or meetup to explore practical applications, industry trends, and best practices.
Browse courses on
Machine Learning Applications
Show steps
-
Research and identify upcoming machine learning workshops or meetups in your area.
-
Register for the event and prepare by reviewing the agenda and speaker profiles.
-
Attend the workshop or meetup, actively participate in discussions, and ask questions.
-
Connect with other attendees and industry professionals to expand your network.
Develop a Machine Learning Project Portfolio
Show steps
Showcase your machine learning skills and gain valuable experience by developing a portfolio of projects that demonstrate your abilities in data preparation, model building, and evaluation.
Browse courses on
Machine Learning Projects
Show steps
-
Identify a problem or challenge that can be addressed using machine learning.
-
Gather and prepare the necessary data for your project.
-
Choose appropriate machine learning algorithms and build models to solve the problem.
-
Evaluate the performance of your models and make necessary adjustments.
-
Document your project, including the problem statement, data used, models developed, and results obtained.
Build a machine learning model video tutorial
Show steps
Create a concise and engaging video tutorial that explains the process of building and evaluating a machine learning model, covering topics like data preprocessing, model selection, and hyperparameter tuning.
Show steps
-
Choose a specific machine learning task and dataset.
-
Develop a comprehensive plan for your video tutorial.
-
Record and edit your video content.
-
Upload and promote your video tutorial on relevant platforms.
-
Gather feedback and make improvements to your tutorial.
Review computer programming
Show steps
Revisit fundamental computer programming concepts such as variables, data structures, and control flow, which are essential for understanding machine learning algorithms and implementing them in code.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review online tutorials or books on basic programming.
- Practice writing simple programs and solving coding exercises.
- Focus on understanding the underlying concepts and their application in machine learning.
- Complete online coding challenges to test your understanding.
Organize and review course materials
Show steps
Regularly review and organize lecture notes, assignments, and other course materials to improve understanding, retention, and preparation for assessments.
Show steps
Show steps
Show steps
- Gather all course materials, including notes and resources.
- Create a designated space for organizing materials.
- Review materials periodically, both during and after class.
- Highlight important concepts and make annotations.
Review Data Engineering Fundamentals
Show steps
Refresh your knowledge of core data engineering concepts such as data modeling, data integration, and data pipelines to enhance your understanding of machine learning principles and applications.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Review key data engineering concepts from introductory materials or online resources.
- Practice data modeling techniques using diagramming tools or online platforms.
- Complete hands-on exercises to implement data integration processes.
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Curate a Collection of Machine Learning Resources
Show steps
Enhance your understanding and stay updated on the latest advancements in machine learning by compiling a collection of valuable resources, including online courses, tutorials, articles, and industry blogs.
Browse courses on
Online Courses
Show steps
Show steps
Show steps
- Identify reputable sources of machine learning content, such as online platforms, academic institutions, and industry thought leaders.
- Categorize and organize the resources based on topics, difficulty levels, and relevance to your learning objectives.
- Regularly update your compilation with new and relevant resources to stay current with the field.
Follow online machine learning tutorials
Show steps
Explore and follow online tutorials that guide you through building and deploying machine learning models using popular libraries like TensorFlow and scikit-learn.
Browse courses on
Machine Learning Workflow
Show steps
Show steps
Show steps
- Identify reputable sources for machine learning tutorials.
- Choose tutorials that align with your learning goals.
- Follow the tutorials step-by-step.
- Implement the code examples and experiment with different parameters.
- Refer to the tutorials for reference and troubleshooting support.
Solve Machine Learning Practice Problems
Show steps
Enhance your understanding of machine learning algorithms by solving practice problems and applying your knowledge to real-world scenarios.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Identify a reputable online platform or textbook with machine learning practice problems.
- Choose a specific machine learning algorithm, such as linear regression or decision trees.
- Solve practice problems related to the chosen algorithm, focusing on understanding the underlying concepts.
- Analyze your solutions and identify areas for improvement.
Solve coding challenges
Show steps
Practice solving coding challenges on platforms like LeetCode and HackerRank to improve your understanding of machine learning algorithms and problem-solving skills.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Choose a coding challenge platform.
- Select a machine learning-related challenge.
- Solve the challenge using the techniques learned in the course.
- Review your solution and identify areas for improvement.
- Repeat with different challenges to improve your skills.
Connect with Mentors in the Machine Learning Field
Show steps
Accelerate your learning by seeking guidance from experienced professionals in the machine learning field who can provide personalized advice, support, and industry insights.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Identify potential mentors through industry events, online communities, or professional networks.
- Reach out to potential mentors and express your interest in their guidance.
- Establish clear expectations and goals for the mentoring relationship.
Attend a Machine Learning Workshop or Meetup
Show steps
Expand your knowledge and connect with experts in the field by attending a machine learning workshop or meetup to explore practical applications, industry trends, and best practices.
Browse courses on
Machine Learning Applications
Show steps
Show steps
Show steps
- Research and identify upcoming machine learning workshops or meetups in your area.
- Register for the event and prepare by reviewing the agenda and speaker profiles.
- Attend the workshop or meetup, actively participate in discussions, and ask questions.
- Connect with other attendees and industry professionals to expand your network.
Develop a Machine Learning Project Portfolio
Show steps
Showcase your machine learning skills and gain valuable experience by developing a portfolio of projects that demonstrate your abilities in data preparation, model building, and evaluation.
Browse courses on
Machine Learning Projects
Show steps
Show steps
Show steps
- Identify a problem or challenge that can be addressed using machine learning.
- Gather and prepare the necessary data for your project.
- Choose appropriate machine learning algorithms and build models to solve the problem.
- Evaluate the performance of your models and make necessary adjustments.
- Document your project, including the problem statement, data used, models developed, and results obtained.
Build a machine learning model video tutorial
Show steps
Create a concise and engaging video tutorial that explains the process of building and evaluating a machine learning model, covering topics like data preprocessing, model selection, and hyperparameter tuning.
Show steps
Show steps
Show steps
- Choose a specific machine learning task and dataset.
- Develop a comprehensive plan for your video tutorial.
- Record and edit your video content.
- Upload and promote your video tutorial on relevant platforms.
- Gather feedback and make improvements to your tutorial.
Career center
Learners who complete How Google does Machine Learning will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use machine learning to analyze large datasets and extract meaningful insights. This course provides a comprehensive overview of machine learning, from the basics to advanced concepts. It also covers best practices for implementing machine learning in real-world scenarios using Vertex AI. This course will help you build a solid foundation in machine learning and prepare you for a successful career as a Data Scientist.
AI Engineer
AI Engineer
AI Engineers design, develop, and maintain AI systems. This course provides a broad overview of machine learning and AI, as well as best practices for implementing AI in real-world scenarios. It also covers the ethical implications of AI and how to mitigate bias in AI systems. This course will help you build a solid foundation in AI and prepare you for a successful career as an AI Engineer.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. This course provides a hands-on introduction to machine learning development with Vertex AI. You will learn how to build, train, and evaluate machine learning models using Vertex AI's powerful tools and services. This course will help you gain the skills and knowledge you need to succeed as a Machine Learning Engineer.
Data Analyst
Data Analyst
Data Analysts use machine learning to analyze data and extract insights. This course provides a comprehensive overview of machine learning, from the basics to advanced concepts. It also covers best practices for implementing machine learning in real-world scenarios using Vertex AI. This course will help you build a solid foundation in machine learning and prepare you for a successful career as a Data Analyst.
Product Manager
Product Manager
Product Managers use machine learning to improve the user experience and drive product growth. This course provides a basic overview of machine learning and how it can be used to solve product problems. It also covers best practices for implementing machine learning in real-world scenarios. This course will help you build a foundation in machine learning and prepare you for a successful career as a Product Manager.
Business Analyst
Business Analyst
Business Analysts use machine learning to analyze business data and identify opportunities for improvement. This course provides a basic overview of machine learning and how it can be used to solve business problems. It also covers best practices for implementing machine learning in real-world scenarios. This course will help you build a foundation in machine learning and prepare you for a successful career as a Business Analyst.
Human Resources Manager
Human Resources Manager
Human Resources Managers use machine learning to improve the hiring, training, and development of employees. This course provides a basic overview of machine learning and how it can be used to solve human resources problems. It also covers best practices for implementing machine learning in real-world scenarios. This course will help you build a foundation in machine learning and prepare you for a successful career as a Human Resources Manager.
Financial Analyst
Financial Analyst
Financial Analysts use machine learning to analyze financial data and make investment decisions. This course provides a basic overview of machine learning and how it can be used to solve financial problems. It also covers best practices for implementing machine learning in real-world scenarios. This course will help you build a foundation in machine learning and prepare you for a successful career as a Financial Analyst.
Operations Manager
Operations Manager
Operations Managers use machine learning to improve the efficiency and effectiveness of business operations. This course provides a basic overview of machine learning and how it can be used to solve operational problems. It also covers best practices for implementing machine learning in real-world scenarios. This course will help you build a foundation in machine learning and prepare you for a successful career as an Operations Manager.
Consultant
Consultant
Consultants use machine learning to help businesses solve problems and improve performance. This course provides a basic overview of machine learning and how it can be used to solve business problems. It also covers best practices for implementing machine learning in real-world scenarios. This course will help you build a foundation in machine learning and prepare you for a successful career as a Consultant.
Risk Manager
Risk Manager
Risk Managers use machine learning to identify and mitigate risks. This course provides a basic overview of machine learning and how it can be used to solve risk management problems. It also covers best practices for implementing machine learning in real-world scenarios. This course will help you build a foundation in machine learning and prepare you for a successful career as a Risk Manager.
Researcher
Researcher
Researchers use machine learning to conduct scientific research and develop new technologies. This course provides a basic overview of machine learning and how it can be used to solve research problems. It also covers best practices for implementing machine learning in real-world scenarios. This course will help you build a foundation in machine learning and prepare you for a successful career as a Researcher.
Sales Manager
Sales Manager
Sales Managers use machine learning to identify and close new sales opportunities. This course provides a basic overview of machine learning and how it can be used to solve sales problems. It also covers best practices for implementing machine learning in real-world scenarios. This course will help you build a foundation in machine learning and prepare you for a successful career as a Sales Manager.
Marketing Manager
Marketing Manager
Marketing Managers use machine learning to target and personalize marketing campaigns. This course provides a basic overview of machine learning and how it can be used to solve marketing problems. It also covers best practices for implementing machine learning in real-world scenarios. This course will help you build a foundation in machine learning and prepare you for a successful career as a Marketing Manager.
Software Engineer
Software Engineer
Software Engineers use machine learning to develop new and innovative software applications. This course provides a basic overview of machine learning and how it can be used to solve software engineering problems. It also covers best practices for implementing machine learning in real-world scenarios. This course will help you build a foundation in machine learning and prepare you for a successful career as a Software Engineer.
For more career information including salaries, visit:
OpenCourser.com/course/efam6r/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.
Provides a statistical approach to machine learning, covering both supervised and unsupervised machine learning.
Provides a statistical approach to machine learning and is commonly used as a textbook in academic institutions.
Practical guide to machine learning and uses the most common Python libraries for machine learning
Provides a probabilistic approach to machine learning, focusing on Bayesian models and their applications.
Presents a unified framework for reinforcement learning and covers a wide range of topics.
Teaches the reader how to use Python for machine learning and covers a wide range of topics.
Teaches the reader how to use R, a popular language for statistical analysis and machine learning.
Provides a concise and accessible introduction to machine learning.
Save
Presents data mining concepts, algorithms, and techniques and serves as a reference book for data mining.
Mathematical introduction to machine learning and covers the theoretical aspects of machine learning.
Is practically oriented and provides a guide to deep learning with Keras, a popular Python library for machine learning
Teaches the reader how to use Python, which popular language for machine learning.
Is geared toward software engineers who want to apply Machine Learning to their work.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/efam6r/how
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Effort
1 week of study, 8-10 hours/week
Level
Introductory
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:
Good to know
Provides a comprehensive overview of machine learning and its applications in various industries
Features instructors from Google Cloud, who are recognized experts in machine learning
Introduces Google Vertex AI, a platform for building, training, and deploying machine learning models
Explores the five phases of implementing machine learning use cases and emphasizes the importance of following them
Raises awareness about potential biases in machine learning systems and offers guidance on recognizing them
Requires learners to have a basic understanding of machine learning concepts
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