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Activities in these self-paced labs are derived from the exercises from the book Data Science on Google Cloud Platform by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this Google Cloud Labs Series, covering chapter 9 through the end of the book,...
Read more
Activities in these self-paced labs are derived from the exercises from the book Data Science on Google Cloud Platform by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this Google Cloud Labs Series, covering chapter 9 through the end of the book,...
Read more
Activities in these self-paced labs are derived from the exercises from the book Data Science on Google Cloud Platform by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this Google Cloud Labs Series, covering chapter 9 through the end of the book, you run full-fledged machine learning jobs with state-of-the-art tools and real-world data sets, all using Google Cloud tools and services.
Note: you will have timed access to the online environment. You will need to complete the lab within the allotted time.
Register for this course and see more details by visiting:
OpenCourser.com/course/laok2p/data
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what to watch for , and
possible dealbreakers
Taught by Google Cloud Training, a recognized provider of cloud-based training
Uses real-world datasets and cutting-edge tools, providing practical experience
Covers advanced machine learning techniques and concepts, suitable for experienced learners
Timed lab access may be restrictive for some learners
Assumes prior knowledge of Google Cloud Platform and machine learning fundamentals
Part of a series, encouraging learners to complete other courses in the sequence
Save this course
Save Data Science on Google Cloud: Machine Learning to your list so you can find it easily later:
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Reviews summary
Outdated course: poorly maintained
This course has a significant number of negative reviews indicating that the course may be out of date and not properly maintained. Students may have issues with the labs and may not be able to complete the course due to inaccessible or broken labs. Technical support may also not be helpful.
Course labs are out of date.
"this course is not update with google cloud"
Labs are broken and support is poor.
"The Labs are broken and there is no support from qwiklabs."
"Don't waste your time."
Required labs are inaccessible.
"Required lab inaccessible for months."
"There is currently no way to complete this course."
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 Data Science on Google Cloud: Machine Learning with these
activities:
Review Linear Algebra and Calculus Concepts
Show steps
Strengthen your foundational skills in linear algebra and calculus to enhance your understanding of machine learning algorithms.
Browse courses on
Linear Algebra
Show steps
-
Review textbooks or online resources on linear algebra and calculus.
-
Solve practice problems to test your understanding.
Read 'Data Science on Google Cloud Platform'
Show steps
Gain in-depth knowledge of the concepts covered in this course by reading the accompanying textbook.
View
Architecting Data and Machine Learning Platforms
on Amazon
Show steps
-
Read the relevant chapters that align with the course content.
-
Take notes and summarize the key concepts.
Explore Google Cloud Machine Learning Tutorials
Show steps
Enhance your practical skills by following guided tutorials on Google Cloud Machine Learning Tools and Services.
Browse courses on
Google Cloud Tools
Show steps
-
Identify a tutorial relevant to the course.
-
Follow the tutorial step-by-step to set up and use Google Cloud tools.
-
Implement the concepts covered in the tutorial in your own projects.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a Study Group or Discussion Forum
Show steps
Engage with other students and experts to enhance your understanding and gain diverse perspectives.
Show steps
-
Join an online study group or discussion forum.
-
Participate in discussions and ask questions.
-
Collaborate with others on projects or assignments.
Create a Tutorial or Blog Post on a Machine Learning Topic
Show steps
Enhance your comprehension and communication skills by creating a tutorial or blog post that explains a machine learning concept or technique.
Show steps
-
Choose a specific machine learning topic to focus on.
-
Research and gather information on the topic.
-
Write a clear and concise tutorial or blog post.
-
Share your content with others and invite feedback.
Solve Practice Problems
Show steps
Practice problem-solving will enhance your understanding of the core concepts of machine learning algorithms and techniques.
Browse courses on
Machine Learning Algorithms
Show steps
-
Identify a practice problem relevant to the course content.
-
Solve the problem using the techniques covered in the course.
-
Review your solution and identify areas for improvement.
Build a Machine Learning Model using Google Cloud
Show steps
Apply the concepts learned in this course by building your own machine learning models using real-world data.
Browse courses on
Machine Learning Projects
Show steps
-
Choose a dataset and define the problem statement.
-
Select and apply suitable machine learning algorithms.
-
Evaluate the performance of your model using metrics relevant to the problem.
-
Deploy your model using Google Cloud Platform.
Mentor a Junior Learner
Show steps
Reinforce your understanding by mentoring a junior learner and guiding their learning journey.
Show steps
-
Identify opportunities to mentor students or colleagues.
-
Provide support and guidance on machine learning concepts.
-
Review and provide feedback on projects or assignments.
Review Linear Algebra and Calculus Concepts
Show steps
Strengthen your foundational skills in linear algebra and calculus to enhance your understanding of machine learning algorithms.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review textbooks or online resources on linear algebra and calculus.
- Solve practice problems to test your understanding.
Read 'Data Science on Google Cloud Platform'
Show steps
Gain in-depth knowledge of the concepts covered in this course by reading the accompanying textbook.
View
Architecting Data and Machine Learning Platforms
on Amazon
Show steps
Show steps
Show steps
- Read the relevant chapters that align with the course content.
- Take notes and summarize the key concepts.
Explore Google Cloud Machine Learning Tutorials
Show steps
Enhance your practical skills by following guided tutorials on Google Cloud Machine Learning Tools and Services.
Browse courses on
Google Cloud Tools
Show steps
Show steps
Show steps
- Identify a tutorial relevant to the course.
- Follow the tutorial step-by-step to set up and use Google Cloud tools.
- Implement the concepts covered in the tutorial in your own projects.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a Study Group or Discussion Forum
Show steps
Engage with other students and experts to enhance your understanding and gain diverse perspectives.
Show steps
Show steps
Show steps
- Join an online study group or discussion forum.
- Participate in discussions and ask questions.
- Collaborate with others on projects or assignments.
Create a Tutorial or Blog Post on a Machine Learning Topic
Show steps
Enhance your comprehension and communication skills by creating a tutorial or blog post that explains a machine learning concept or technique.
Show steps
Show steps
Show steps
- Choose a specific machine learning topic to focus on.
- Research and gather information on the topic.
- Write a clear and concise tutorial or blog post.
- Share your content with others and invite feedback.
Solve Practice Problems
Show steps
Practice problem-solving will enhance your understanding of the core concepts of machine learning algorithms and techniques.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Identify a practice problem relevant to the course content.
- Solve the problem using the techniques covered in the course.
- Review your solution and identify areas for improvement.
Build a Machine Learning Model using Google Cloud
Show steps
Apply the concepts learned in this course by building your own machine learning models using real-world data.
Browse courses on
Machine Learning Projects
Show steps
Show steps
Show steps
- Choose a dataset and define the problem statement.
- Select and apply suitable machine learning algorithms.
- Evaluate the performance of your model using metrics relevant to the problem.
- Deploy your model using Google Cloud Platform.
Mentor a Junior Learner
Show steps
Reinforce your understanding by mentoring a junior learner and guiding their learning journey.
Show steps
Show steps
Show steps
- Identify opportunities to mentor students or colleagues.
- Provide support and guidance on machine learning concepts.
- Review and provide feedback on projects or assignments.
Career center
Learners who complete Data Science on Google Cloud: Machine Learning will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
As a Data Analyst, you'll use data to identify trends and patterns. You'll work with data scientists to develop and deploy machine learning models. You'll also work with business stakeholders to communicate the results of your analysis. This course provides a foundation in the skills and knowledge you need to be successful as a Data Analyst. You'll learn how to use Google Cloud tools and services to collect, clean, and analyze data. You'll also learn how to use machine learning models to identify trends and patterns.
Data Scientist
Data Scientist
As a Data Scientist, you'll use data to solve business problems. You'll work with data engineers to collect and clean data. You'll also work with machine learning engineers to develop and deploy machine learning models. This course provides a foundation in the skills and knowledge you need to be successful as a Data Scientist. You'll learn how to use Google Cloud tools and services to collect, clean, and analyze data. You'll also learn how to use machine learning models to solve business problems.
Machine Learning Engineer
Machine Learning Engineer
As a Machine Learning Engineer, you'll design, develop, and maintain machine learning models. You'll work with data scientists to identify business problems that can be solved with machine learning. You'll also work with software engineers to integrate machine learning models into production systems. This course provides a foundation in the skills and knowledge you need to be successful as a Machine Learning Engineer. You'll learn how to use Google Cloud tools and services to train, evaluate, and deploy machine learning models.
Software Engineer
Software Engineer
As a Software Engineer, you'll design, develop, and maintain software applications. You'll work with data scientists and machine learning engineers to integrate machine learning models into production systems. This course provides a foundation in the skills and knowledge you need to be successful as a Software Engineer. You'll learn how to use Google Cloud tools and services to develop and deploy software applications.
Researcher
Researcher
As a Researcher, you'll conduct research in the field of machine learning. You'll develop new algorithms and techniques for solving machine learning problems. This course may be useful for Researchers who want to learn more about machine learning and how it can be used to advance the field.
Entrepreneur
Entrepreneur
As an Entrepreneur, you'll start and run your own business. You'll use your knowledge of machine learning to develop new products and services. This course may be useful for Entrepreneurs who want to learn more about machine learning and how it can be used to start and grow a successful business.
Consultant
Consultant
As a Consultant, you'll work with clients to solve business problems. You'll use your expertise in machine learning to help clients improve their operations, make better decisions, and increase their profits.
Teacher
Teacher
As a Teacher, you'll teach students about machine learning. You'll also develop and deliver учебные планы. This course may be useful for Teachers who want to learn more about machine learning and how it can be used to improve their teaching.
Financial Analyst
Financial Analyst
As a Financial Analyst, you'll analyze financial data to make investment recommendations. You'll also work with companies to help them raise capital. This course may be useful for Financial Analysts who want to learn more about machine learning and how it can be used to improve financial analysis.
Sales Manager
Sales Manager
As a Sales Manager, you'll lead and motivate a team of sales representatives. You'll also work with marketing teams to generate leads and close deals. This course may be useful for Sales Managers who want to learn more about machine learning and how it can be used to improve sales performance.
Business Analyst
Business Analyst
As a Business Analyst, you'll work with stakeholders to identify and solve business problems. You'll use data to analyze the current state of a business and to develop recommendations for improvement. This course may be useful for Business Analysts who want to learn more about machine learning and how it can be used to solve business problems.
Marketing Manager
Marketing Manager
As a Marketing Manager, you'll develop and execute marketing campaigns. You'll also work with sales teams to generate leads and close deals. This course may be useful for Marketing Managers who want to learn more about machine learning and how it can be used to improve marketing campaigns.
Product Manager
Product Manager
As a Product Manager, you'll work with engineers and designers to develop and launch new products. You'll also work with marketing and sales teams to promote and sell those products. This course may be useful for Product Managers who want to learn more about machine learning and how it can be used to improve products.
Operations Manager
Operations Manager
As an Operations Manager, you'll oversee the day-to-day operations of a business. You'll also work with other departments to ensure that the business is running smoothly. This course may be useful for Operations Managers who want to learn more about machine learning and how it can be used to improve operational efficiency.
Doctor
Doctor
As a Doctor, you'll use machine learning to diagnose and treat diseases. You'll also develop new drugs and treatments. This course may be useful for Doctors who want to learn more about machine learning and how it can be used to improve patient care.
For more career information including salaries, visit:
OpenCourser.com/course/laok2p/data
Reading list
We've selected 12 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
Data Science on Google Cloud: Machine Learning.
Provides a comprehensive overview of data science concepts and techniques on Google Cloud Platform. It covers the entire data science workflow, from data preparation and modeling to training and deploying machine learning models.
Provides a hands-on introduction to deep learning using Python. It covers the basics of deep learning, as well as how to build and train deep learning models.
Provides a comprehensive guide to TensorFlow, a popular open-source machine learning library. It covers the basics of TensorFlow, as well as how to build and train deep learning models using TensorFlow.
Provides a hands-on introduction to machine learning using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It covers the basics of machine learning, as well as how to build and train machine learning models using these libraries.
Provides a comprehensive introduction to machine learning using R, a popular statistical programming language. It covers the basics of machine learning, as well as how to build and train machine learning models using R.
Provides a comprehensive overview of statistical learning methods, including linear regression, logistic regression, decision trees, and support vector machines. It valuable reference for anyone who wants to learn about the theoretical foundations of machine learning.
Provides a comprehensive overview of machine learning from a Bayesian and optimization perspective. It covers the basics of machine learning, as well as more advanced topics such as Bayesian inference, variational inference, and optimization methods.
Save
Provides a comprehensive overview of deep learning, including the latest advances in the field. It valuable reference for anyone who wants to learn about the theoretical foundations of deep learning.
Provides a comprehensive overview of pattern recognition and machine learning, including the latest advances in the field. It valuable reference for anyone who wants to learn about the theoretical foundations of pattern recognition and machine learning.
Provides a comprehensive overview of machine learning, including the latest advances in the field. It valuable reference for anyone who wants to learn about the theoretical foundations of machine learning.
Save
Practical guide to machine learning in Chinese. It covers the basics of machine learning, as well as how to build and train machine learning models using popular Python libraries.
Save
Comprehensive overview of machine learning in Chinese. It covers the basics of machine learning, as well as more advanced topics such as deep learning and reinforcement learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/laok2p/data
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Time
7 hours
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Good to know
Taught by Google Cloud Training, a recognized provider of cloud-based training
Uses real-world datasets and cutting-edge tools, providing practical experience
Covers advanced machine learning techniques and concepts, suitable for experienced learners
Timed lab access may be restrictive for some learners
Assumes prior knowledge of Google Cloud Platform and machine learning fundamentals
Part of a series, encouraging learners to complete other courses in the sequence
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