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How Google does Machine Learning

4.6 Filled star Filled star Filled star Filled star Half star
Based on 7,267 ratings
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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.

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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

Traffic lights

Read about what's good
what should give you pause
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

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Reviews summary

Google's ml approach and vertex ai overview

According to learners, this course provides a valuable insight into how Google approaches Machine Learning, moving from concept to production. Many find the strategic overview and discussion of best practices particularly useful, especially for understanding industry-scale ML development. The course introduces Vertex AI and covers essential topics like Responsible AI. While providing a solid foundation and a practical overview, some students note that the course is more high-level and could benefit from more technical depth and detailed code examples for practitioners. Overall, it's seen as a good course for understanding the ML lifecycle from a major tech company's perspective, though it may require supplemental learning for hands-on implementation details.
Introduces Google's ML platform.
"The sections on Vertex AI gave me a decent introduction to the platform's capabilities."
"Covers the basics of using Vertex AI for different parts of the ML workflow."
"Helpful for understanding where Vertex AI fits into the ML development process."
"Got a good initial grasp of the Vertex AI ecosystem."
Highlights ethical considerations in ML.
"Appreciated the focus on bias and fairness in ML systems and how to mitigate them."
"The module on responsible AI was insightful and highlighted important ethical considerations."
"Good coverage of the ethical challenges in AI development, a crucial topic."
"Provided a solid introduction to the importance of responsible ML practices."
Offers unique industry perspective.
"Gave me a great understanding of how large companies like Google think about ML projects."
"The insights into Google's internal practices and strategies were very valuable."
"Appreciated learning the 'why' behind Google's ML strategy rather than just the 'how'."
"It's fascinating to get a glimpse into how a tech giant handles ML development at scale."
Focuses on strategy, less on code.
"This course is very high-level, good for managers but not engineers wanting code."
"I was expecting more technical implementation details, but it's mostly strategic and conceptual."
"It's a good overview, but you'll definitely need other resources for deep dives or coding."
"Provides a framework and concepts but isn't suitable for hands-on learning without supplements."

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
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
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
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
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
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
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
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
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
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
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
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.

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 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.
See salaries and explore the career path for Data Scientist
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.
See salaries and explore the career path for Machine Learning 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.
See salaries and explore the career path for AI Engineer
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.
See salaries and explore the career path for Data 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.
See salaries and explore the career path for Business Analyst
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.
See salaries and explore the career path for Product Manager
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.
See salaries and explore the career path for Software Engineer
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.
See salaries and explore the career path for Marketing 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.
See salaries and explore the career path for Sales Manager
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.
See salaries and explore the career path for Financial Analyst
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.
See salaries and explore the career path for Operations 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.
See salaries and explore the career path for Risk 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.
See salaries and explore the career path for Human Resources Manager
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.
See salaries and explore the career path for Consultant
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.
See salaries and explore the career path for Researcher

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.
Pattern Recognition and Machine Learning
Save
Provides a statistical approach to machine learning, covering both supervised and unsupervised machine learning.
Pattern Recognition and Machine Learning...
Hardcover
$$$
Pattern Recognition and Machine Learning...
Paperback
$$$
Pattern Recognition and Machine Learning (text...
Unknown Binding
$$$$
Cover image
An Introduction to Statistical Learning
Save
Provides a statistical approach to machine learning and is commonly used as a textbook in academic institutions.
An Introduction to Statistical Learning
Paperback
Check
An Introduction to Statistical Learning
Kindle Edition
Check
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
Practical guide to machine learning and uses the most common Python libraries for machine learning
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
$$
Cover image
Probabilistic Machine Learning
Save
Provides a probabilistic approach to machine learning, focusing on Bayesian models and their applications.
Probabilistic Machine Learning: Advanced Topics...
Hardcover
$$$$
Cover image
Reinforcement Learning, second edition
Save
Presents a unified framework for reinforcement learning and covers a wide range of topics.
Reinforcement Learning, second edition: An...
Hardcover
$$$
Reinforcement Learning, second edition: An...
Kindle Edition
$$$
Machine Learning Con Pytorch Y Scikit Lean
Save
Teaches the reader how to use Python for machine learning and covers a wide range of topics.
(Español) Machine Learning con PyTorch y Scikit-Learn:...
Paperback
$$$
Cover image
Machine Learning with R
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Teaches the reader how to use R, a popular language for statistical analysis and machine learning.
Machine Learning with R - Fourth Edition: Learn...
Paperback
$$
Machine Learning with R - Second Edition: Expert...
Kindle Edition
$$
Machine Learning with R - Third Edition: Expert...
Paperback
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(Italiano) Machine Learning con R: Conoscere le tecniche per...
Kindle Edition
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R: Unleash Machine Learning Techniques
Kindle Edition
$$$
Machine Learning with R: Learn How to Use R to...
Paperback
$$
(中文) Machine learning and R language (3rd edition of the...
Paperback
$$
Cover image
Machine Learning Engineering
Save
Provides a concise and accessible introduction to machine learning.
Machine Learning Engineering
Paperback
$$
Machine Learning Engineering
Kindle Edition
$$
Data Mining
Save
Presents data mining concepts, algorithms, and techniques and serves as a reference book for data mining.
Data Mining: Concepts and Techniques (The Morgan...
Kindle Edition
$$$
Data Mining: Concepts and Techniques (The Morgan...
Hardcover
$$$
Data Mining: Concepts and Techniques, Second...
Hardcover
$$
Data Mining: Know It All
Hardcover
$$$
Understanding Machine Learning
Save
Mathematical introduction to machine learning and covers the theoretical aspects of machine learning.
Understanding Machine Learning: From Theory to...
Hardcover
$$$
Understanding Machine Learning: From Theory to...
Kindle Edition
$$
Cover image
Deep Learning with Python, Second Edition
Save
Is practically oriented and provides a guide to deep learning with Keras, a popular Python library for machine learning
Deep Learning with Python, Second Edition
Paperback
$$
Deep Learning with Python, Second Edition
Kindle Edition
$$
Cover image
Automate the Boring Stuff with Python, 2nd Edition
Save
Teaches the reader how to use Python, which popular language for machine learning.
Automate the Boring Stuff with Python, 2nd Edition:...
Paperback
$$
Automate the Boring Stuff with Python, 2nd Edition:...
Kindle Edition
$$
Cover image
Machine Learning for Hackers
Save
Is geared toward software engineers who want to apply Machine Learning to their work.
Machine Learning for Hackers: Case Studies and...
Paperback
$$
Machine Learning for Hackers: Case Studies and...
Kindle Edition
$$

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Rating
4.6 Filled star Filled star Filled star Filled star Half star
Effort
Approx. 11 hours
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:
  1. Введение в биоинформатику (Introduction to Bioinformatics)
  2. Launching into Machine Learning
  3. Русский язык как инструмент успешной коммуникации
  4. How Google does Machine Learning (viewing)
  5. Feature Engineering
  6. TensorFlow on Google Cloud
  7. Machine Learning in the Enterprise
Traffic lights
Read about what's good
what should give you pause
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
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