We may earn an affiliate commission when you visit our partners.
Google Cloud Training
Business professionals in non-technical roles have a unique opportunity to lead or influence machine learning projects. If you have questions about machine learning and want to understand how to use it, without the technical jargon, this course is for you. Learn how to translate business problems into machine learning use cases and vet them for feasibility and impact. Find out how you can discover unexpected use cases, recognize the phases of an ML project and considerations within each, and gain confidence to propose a custom ML use case to your team or leadership or translate the requirements to a technical team.
Register for this course and see more details by visiting:
OpenCourser.com/course/lmy0wn/managing
Two deals to help you save
Save money when you learn. Here are two deals and offers that may be relevant to this course.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Valid until July 14
Coursera Plus Annual
Work toward what's next for your career with access to 10,000+ programs. Discover emerging skills and save.
Up to
40%
off
Get offer
Valid until August 30
Google AI App Builder
Learn how to use Gemini API and API Studio with a three-course series from Google DeepMind
Take
100%
off
Get offer
What's inside
Syllabus
Module 1: Introduction
Welcome to the course! In this module, you'll meet the instructor and learn about the course content and how to get started.
Read more
Syllabus
Module 1: Introduction
Welcome to the course! In this module, you'll meet the instructor and learn about the course content and how to get started.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Explores real-world applications of ML, making it relevant to business professionals
Teaches how to identify feasible ML projects and evaluate their business impact
Develops business acumen in ML, enabling professionals to lead and influence ML projects
Introduces industry-standard tools and practices, such as Google Cloud's Vision API and AutoML Vision
Highlights ethical considerations in ML, fostering a responsible approach to its use
Provides hands-on labs to reinforce key concepts and practical skills
Save this course
Create your own learning path.
Save this course to your list so you can find it easily later.
Save
Save
Reviews summary
Managing ml projects for business leaders
According to the course's design and syllabus, this program is built for business professionals in non-technical roles who need to understand and manage ML projects. It appears to excel in explaining ML concepts without technical jargon, focusing instead on identifying business value and potential use cases. Learners can expect to gain a clear understanding of the ML project lifecycle and key management considerations. The course includes hands-on exposure to Google Cloud tools like AutoML and BigQuery ML through demos and labs, offering practical experience for non-coders. It also covers important topics like responsible AI and ethics. While designed for a non-technical audience, those seeking deep technical details might find it too high-level.
Includes important ethical considerations
"The section on bias in ML was eye-opening and very relevant today."
"I appreciate that the course covers the ethical implications of ML."
"Understanding fairness is crucial for anyone involved in ML projects."
Practical demos and labs with GCP tools
"Getting to try out AutoML Vision in the lab was a highlight."
"The BigQuery ML lab showed me how non-coders can interact with models."
"Demos of the Vision API helped me see real-world use cases."
Covers the ML project lifecycle
"The course gave me a clear picture of the ML project lifecycle."
"Understanding the different phases helps me communicate better with my technical team."
"I now feel more equipped to oversee an ML initiative."
Focuses on real-world business applications
"I learned how to spot potential ML projects in my own company."
"The framework for assessing ML feasibility was very useful."
"Understanding how to translate business needs into ML problems is a key takeaway."
Explains ML for non-technical roles
"It was great to learn about ML without getting bogged down in code or complex math."
"The course explained concepts clearly for someone like me in a business role."
"Finally, an ML course I can understand as a manager!"
May be too basic for technical learners
"As a data scientist, I found the technical details were too high-level."
"It's great for managers, but don't expect to learn how to build models."
"I wish they had gone a bit deeper into the technical challenges."
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 Managing Machine Learning Projects with Google Cloud with these
activities:
Compile a collection of resources related to machine learning for future reference
Show steps
Stay organized and efficient by gathering relevant materials such as lecture notes, articles, tutorials, and datasets in one easily accessible location.
Browse courses on
Knowledge Base
Show steps
-
Create a dedicated folder or digital notebook to store your resources
-
Systematically gather and categorize materials based on topics or themes
-
Use descriptive naming conventions for easy retrieval later on
Review fundamentals of machine learning and data science basics
Show steps
Solidify your foundational knowledge of machine learning and data science principles, which will support topics in this course such as data analysis and model building.
Browse courses on
Machine Learning
Show steps
-
Revisit core concepts of machine learning algorithms, such as supervised and unsupervised learning.
-
Refresh your understanding of data manipulation and preprocessing techniques, such as data cleaning, feature engineering, and normalization.
-
Review statistical concepts such as probability, hypothesis testing, and regression analysis.
Follow Google's ML Crash Course
Show steps
Solidify the basics of ML and get an overview of the most common ML algorithms and techniques.
Browse courses on
ML Fundamentals
Show steps
-
Go to https://developers.google.com/machine-learning/crash-course
-
Complete all the tutorials in the crash course
-
Take the quiz at the end of each tutorial to test your understanding
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Refresh knowledge of machine learning fundamentals
Show steps
Review the basic concepts of ML before starting the course to strengthen your understanding throughout the rest of the course.
Browse courses on
Machine Learning Fundamentals
Show steps
-
Review the course syllabus and identify the topics that cover ML fundamentals.
-
Read through your notes or study materials from previous courses or resources on ML fundamentals.
-
Complete practice questions or exercises related to ML fundamentals.
Explore online tutorials or workshops to supplement your learning
Show steps
Enrich your knowledge by accessing a wealth of online tutorials and workshops tailored to different aspects of machine learning, broadening your understanding.
Show steps
-
Identify areas where you want to enhance your knowledge or skills
-
Search for reputable online tutorials or workshops that align with your learning objectives
-
Follow the tutorials step-by-step, taking notes and experimenting with the provided code examples
Explore tutorials on specific ML use cases
Show steps
Seek out and follow tutorials that demonstrate real-world applications of ML to gain a deeper understanding of its capabilities and limitations.
Show steps
-
Identify specific business problems or industries that interest you.
-
Search for tutorials or articles that showcase ML solutions for these problems or industries.
-
Follow the tutorials, paying attention to the implementation details and the results achieved.
Create an ML use case proposal
Show steps
Start thinking about how you can use ML to solve real-world problems by creating a proposal for an ML use case.
Show steps
-
Identify a business problem that can be solved with ML
-
Research different ML techniques and algorithms
-
Develop a plan for implementing the ML solution
-
Create a presentation or document to pitch your ML use case proposal
Practice applying machine learning algorithms to real-world datasets
Show steps
Supplement your theoretical knowledge by actively working through real-world examples of machine learning algorithm implementation.
Browse courses on
Machine Learning Algorithms
Show steps
-
Choose a machine learning algorithm to practice (e.g. regression, classification, clustering)
-
Find a suitable dataset for your chosen algorithm
-
Implement the algorithm using a programming language of your choice
-
Evaluate the performance of your model on the dataset
-
Tweak the algorithm's parameters and observe the impact on performance
Build an ML model with AutoML
Show steps
Gain hands-on experience with ML model building using Google's AutoML platform.
Browse courses on
AutoML
Show steps
-
Go to https://cloud.google.com/automl
-
Create an AutoML account
-
Choose a dataset to train your model on
-
Select the type of ML model you want to build
-
Train your model
-
Evaluate the performance of your model
-
Deploy your model
Participate in a study group or discussion forum to exchange knowledge and insights
Show steps
Engage with peers to strengthen your understanding and gain diverse perspectives on machine learning topics and applications.
Browse courses on
Collaborative Learning
Show steps
-
Join or create a study group consisting of fellow students enrolled in this course
-
Participate actively in group discussions, sharing ideas and asking questions to deepen your understanding
-
Attend online or in-person discussion forums to engage with a broader community of machine learning enthusiasts and practitioners
Write a blog post or article to share your insights on a machine learning topic
Show steps
Solidify your understanding by articulating your learning in written form, fostering critical thinking and effective communication.
Browse courses on
Technical Writing
Show steps
-
Choose a specific machine learning topic that you are knowledgeable about
-
Conduct research to gather relevant information and insights
-
Organize your thoughts and structure your content logically
-
Write a draft, paying attention to clarity, conciseness, and accuracy
-
Proofread and edit your work carefully before publishing
Build a machine learning model to solve a specific business problem
Show steps
Apply your learning by creating a complete machine learning solution that addresses a genuine business challenge, such as customer churn prediction or fraud detection.
Browse courses on
Machine Learning Model
Show steps
-
Define the business problem and the desired outcome of your model
-
Collect and prepare the necessary data for your model
-
Select and train an appropriate machine learning algorithm
-
Evaluate and refine your model's performance based on metrics relevant to the business problem
-
Create a report or presentation to communicate your findings and insights
Compile a collection of resources related to machine learning for future reference
Show steps
Stay organized and efficient by gathering relevant materials such as lecture notes, articles, tutorials, and datasets in one easily accessible location.
Browse courses on
Knowledge Base
Show steps
Show steps
Show steps
- Create a dedicated folder or digital notebook to store your resources
- Systematically gather and categorize materials based on topics or themes
- Use descriptive naming conventions for easy retrieval later on
Review fundamentals of machine learning and data science basics
Show steps
Solidify your foundational knowledge of machine learning and data science principles, which will support topics in this course such as data analysis and model building.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Revisit core concepts of machine learning algorithms, such as supervised and unsupervised learning.
- Refresh your understanding of data manipulation and preprocessing techniques, such as data cleaning, feature engineering, and normalization.
- Review statistical concepts such as probability, hypothesis testing, and regression analysis.
Follow Google's ML Crash Course
Show steps
Solidify the basics of ML and get an overview of the most common ML algorithms and techniques.
Browse courses on
ML Fundamentals
Show steps
Show steps
Show steps
- Go to https://developers.google.com/machine-learning/crash-course
- Complete all the tutorials in the crash course
- Take the quiz at the end of each tutorial to test your understanding
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Refresh knowledge of machine learning fundamentals
Show steps
Review the basic concepts of ML before starting the course to strengthen your understanding throughout the rest of the course.
Browse courses on
Machine Learning Fundamentals
Show steps
Show steps
Show steps
- Review the course syllabus and identify the topics that cover ML fundamentals.
- Read through your notes or study materials from previous courses or resources on ML fundamentals.
- Complete practice questions or exercises related to ML fundamentals.
Explore online tutorials or workshops to supplement your learning
Show steps
Enrich your knowledge by accessing a wealth of online tutorials and workshops tailored to different aspects of machine learning, broadening your understanding.
Show steps
Show steps
Show steps
- Identify areas where you want to enhance your knowledge or skills
- Search for reputable online tutorials or workshops that align with your learning objectives
- Follow the tutorials step-by-step, taking notes and experimenting with the provided code examples
Explore tutorials on specific ML use cases
Show steps
Seek out and follow tutorials that demonstrate real-world applications of ML to gain a deeper understanding of its capabilities and limitations.
Show steps
Show steps
Show steps
- Identify specific business problems or industries that interest you.
- Search for tutorials or articles that showcase ML solutions for these problems or industries.
- Follow the tutorials, paying attention to the implementation details and the results achieved.
Create an ML use case proposal
Show steps
Start thinking about how you can use ML to solve real-world problems by creating a proposal for an ML use case.
Show steps
Show steps
Show steps
- Identify a business problem that can be solved with ML
- Research different ML techniques and algorithms
- Develop a plan for implementing the ML solution
- Create a presentation or document to pitch your ML use case proposal
Practice applying machine learning algorithms to real-world datasets
Show steps
Supplement your theoretical knowledge by actively working through real-world examples of machine learning algorithm implementation.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Choose a machine learning algorithm to practice (e.g. regression, classification, clustering)
- Find a suitable dataset for your chosen algorithm
- Implement the algorithm using a programming language of your choice
- Evaluate the performance of your model on the dataset
- Tweak the algorithm's parameters and observe the impact on performance
Build an ML model with AutoML
Show steps
Gain hands-on experience with ML model building using Google's AutoML platform.
Browse courses on
AutoML
Show steps
Show steps
Show steps
- Go to https://cloud.google.com/automl
- Create an AutoML account
- Choose a dataset to train your model on
- Select the type of ML model you want to build
- Train your model
- Evaluate the performance of your model
- Deploy your model
Participate in a study group or discussion forum to exchange knowledge and insights
Show steps
Engage with peers to strengthen your understanding and gain diverse perspectives on machine learning topics and applications.
Browse courses on
Collaborative Learning
Show steps
Show steps
Show steps
- Join or create a study group consisting of fellow students enrolled in this course
- Participate actively in group discussions, sharing ideas and asking questions to deepen your understanding
- Attend online or in-person discussion forums to engage with a broader community of machine learning enthusiasts and practitioners
Write a blog post or article to share your insights on a machine learning topic
Show steps
Solidify your understanding by articulating your learning in written form, fostering critical thinking and effective communication.
Browse courses on
Technical Writing
Show steps
Show steps
Show steps
- Choose a specific machine learning topic that you are knowledgeable about
- Conduct research to gather relevant information and insights
- Organize your thoughts and structure your content logically
- Write a draft, paying attention to clarity, conciseness, and accuracy
- Proofread and edit your work carefully before publishing
Build a machine learning model to solve a specific business problem
Show steps
Apply your learning by creating a complete machine learning solution that addresses a genuine business challenge, such as customer churn prediction or fraud detection.
Browse courses on
Machine Learning Model
Show steps
Show steps
Show steps
- Define the business problem and the desired outcome of your model
- Collect and prepare the necessary data for your model
- Select and train an appropriate machine learning algorithm
- Evaluate and refine your model's performance based on metrics relevant to the business problem
- Create a report or presentation to communicate your findings and insights
Career center
Learners who complete Managing Machine Learning Projects with Google Cloud will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists analyze data with a variety of tools from statistics and machine learning to uncover insights and make predictions.
Data Analyst
Data Analyst
Data Analysts collect, analyze, interpret, and present data. This course can help Data Analysts develop a foundational understanding of machine learning, which can be leveraged to identify patterns and trends in data, and build predictive models. This course also provides insights into the ethical considerations when utilizing ML.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, deploy, and maintain machine learning systems. This course may be useful for someone pursuing this career path, as it introduces the basics of machine learning, and covers the considerations for building a dataset, evaluating a model, and managing an ML project, which are important tasks for an ML engineer.
AI Engineer
AI Engineer
Artificial Intelligence Engineers research, design, and develop artificial intelligence systems. This course will provide a foundational understanding of the concepts and challenges involved in building machine learning applications.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex business problems. This course may be helpful for an Operations Research Analyst who wants to learn more about the applications of machine learning in operations research. For example, Module 7, which covers managing ML projects successfully, may be helpful for managing ML projects in an operations research context.
Data Architect
Data Architect
Data Architects design and implement data management solutions. This course can provide a foundational understanding of machine learning and its applications, which can be leveraged to design and implement data management solutions that support ML initiatives. For example, knowledge from Module 7 about best practices for managing ML projects may be helpful.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical modeling to analyze and predict financial markets. This course may be helpful for a Quantitative Analyst who wants to learn more about the applications of machine learning in finance. For example, Module 4 covers building and evaluating ML models, which is a key skill for developing trading strategies.
Business Analyst
Business Analyst
Business Analysts identify and define business needs and recommend solutions that leverage technology. This course would allow a Business Analyst to become familiar with machine learning, which is increasingly being used to solve business problems. This may be helpful for identifying opportunities for the application of ML, as well as for understanding the capabilities and limitations of ML when evaluating solutions.
Risk Analyst
Risk Analyst
Risk Analysts identify, assess, and mitigate risks. This course may be helpful for a Risk Analyst who wants to learn more about the applications of machine learning in risk management. For example, Module 5 covers using ML responsibly and ethically, which is critical for building and deploying ML models that are fair and unbiased.
Market Research Analyst
Market Research Analyst
Market Research Analysts collect and analyze data to understand customer needs and trends. This course may be helpful for a Market Research Analyst who wants to learn more about the applications of machine learning in market research. For example, Module 3, which covers defining ML as a practice, may be helpful for understanding how to use ML to collect and analyze data.
Product Manager
Product Manager
Product Managers are responsible for defining the vision, roadmap, and feature set of a product. This course provides Product Managers with an understanding of the business value and considerations within each phase of an ML project, which would allow them to make more informed decisions about how to utilize ML in their products.
Business Consultant
Business Consultant
Business Consultants provide advice and guidance to businesses on how to improve their operations. This course may be useful for a Business Consultant who wants to better understand the potential applications of machine learning and how it can be used to solve business problems.
UX Designer
UX Designer
UX Designers create user interfaces and experiences for websites and applications. This course may be helpful for a UX Designer who is interested in incorporating machine learning into their work. For example, knowledge from Module 4 about evaluating ML models can help ensure that the ML features are user-friendly and effective.
Software Engineer
Software Engineer
Software Engineers apply their knowledge of computers and mathematics to develop and implement software. This course may be helpful for somebody in this role who wants to become involved in developing machine learning applications, but is not planning to become a dedicated ML Engineer.
Data Engineer
Data Engineer
Data Engineers work on designing, installing, maintaining, and managing big data systems. This course teaches some of the core concepts of machine learning that may be helpful for a Data Engineer who has to work with ML algorithms. For example, insights about bias in ML models from Module 5 may be helpful for building and monitoring a data pipeline that serves ML models.
For more career information including salaries, visit:
OpenCourser.com/course/lmy0wn/managing
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
Managing Machine Learning Projects with Google Cloud.
Save
Comprehensive guide to deep learning. It good choice for those who want to learn more about the latest advances in ML.
Classic textbook on statistical learning. It provides a comprehensive overview of the field and good choice for those who want to learn the theoretical foundations of ML.
Provides a probabilistic perspective on machine learning. It good choice for those who want to learn more about the theoretical foundations of ML.
Provides a comprehensive overview of machine learning techniques and algorithms. It good choice for those who want to learn more about the technical aspects of ML.
Save
Classic textbook on pattern recognition and machine learning. It good choice for those who want to learn the theoretical foundations of ML.
Provides a comprehensive overview of machine learning. It good choice for those who want to learn more about the field.
Provides a step-by-step guide to machine learning with R. It good choice for those who want to learn how to use R for ML.
Provides an algorithmic perspective on machine learning. It good choice for those who want to learn more about the algorithms used in ML.
Provides a hands-on introduction to machine learning for those who have no prior knowledge of the field. It is written in a clear and concise style and provides many examples to help readers understand the concepts.
Great introduction to machine learning for those who have no prior knowledge of the field. It is written in a clear and concise style and provides many examples to help readers understand the concepts.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/lmy0wn/managing
Share
Similar courses
Similar courses are unavailable at this time. Please try again later.
Rating
4.6
Effort
Approx. 13 hours
Level
Introductory
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2025 OpenCourser