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Google Cloud Training
This is a self-paced lab that takes place in the Google Cloud console.
Learn the process for partitioning a data set into two separate parts: a training set to develop a model, and a test set to evaluate the accuracy of the model and then independently evaluate predictive models in a repeatable manner.
Register for this course and see more details by visiting:
OpenCourser.com/course/w9jo6d/distributed
What's inside
Syllabus
Distributed Machine Learning with Google Cloud ML
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for learners from any background, this course offers a comprehensive introduction to distributed machine learning with Google Cloud ML. Gain valuable hands-on experience with no prior knowledge required
Practical and engaging, this course provides hands-on experience in the Google Cloud console, allowing learners to apply their understanding directly
Ideal for learners seeking to build their skills in data science and machine learning on the Google Cloud platform
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Distributed Machine Learning with Google Cloud ML with these
activities:
Follow tutorials on machine learning with Google Cloud ML
Show steps
Gain hands-on experience in using Google Cloud ML for machine learning tasks.
Show steps
-
Visit the Google Cloud ML website.
-
Search for tutorials.
-
Select a tutorial that interests you.
-
Follow the steps in the tutorial.
-
Complete the tutorial.
Review 'Machine Learning with R' by Brett Lantz
Show steps
Gain a deeper understanding of machine learning concepts and their application in R.
View
Machine Learning with R - Fourth Edition: Learn...
on Amazon
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Compile a list of resources on machine learning with Google Cloud ML
Show steps
Organize and expand your knowledge base by creating a curated collection of resources on Google Cloud ML.
Show steps
-
Start by creating a new document.
-
Add a title to your document.
-
Include a brief introduction to Google Cloud ML.
-
List resources such as tutorials, documentation, and blog posts.
-
Organize your resources into categories.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice partitioning datasets
Show steps
Practice partitioning datasets into training and test sets to build models and evaluate their accuracy.
Show steps
-
Start by creating two empty lists, one for the training set and one for the test set.
-
Use a random number generator to select a percentage of the data points to be included in the training set.
-
Add the selected data points to the training set.
-
Add the remaining data points to the test set.
-
Repeat steps 2-4 until both the training and test sets contain the desired number of data points.
Attend a machine learning workshop
Show steps
Expand your knowledge and skills by interacting with experts and peers in a workshop setting.
Show steps
-
Find a machine learning workshop that interests you.
-
Register for the workshop.
-
Attend the workshop.
-
Participate in discussions and activities.
-
Network with other attendees.
Participate in a machine learning study group
Show steps
Collaborate with peers to enhance your understanding and reinforce concepts through discussions and problem-solving.
Show steps
-
Find a study group that interests you.
-
Join the study group.
-
Attend study group meetings.
-
Participate in discussions.
-
Work together to solve problems.
Build a machine learning model using Google Cloud ML
Show steps
Put your knowledge into practice by building a real-world machine learning model.
Show steps
-
Start by defining the problem you want to solve.
-
Gather data relevant to your problem.
-
Clean and prepare your data.
-
Train a machine learning model using Google Cloud ML.
-
Evaluate the performance of your model.
Contribute to an open-source machine learning project
Show steps
Make a meaningful contribution to the machine learning community by contributing to open-source projects.
Show steps
-
Find an open-source machine learning project that interests you.
-
Create a GitHub account.
-
Fork the project to your own GitHub account.
-
Make changes to the project.
-
Submit a pull request to the original project.
Follow tutorials on machine learning with Google Cloud ML
Show steps
Gain hands-on experience in using Google Cloud ML for machine learning tasks.
Show steps
Show steps
Show steps
- Visit the Google Cloud ML website.
- Search for tutorials.
- Select a tutorial that interests you.
- Follow the steps in the tutorial.
- Complete the tutorial.
Review 'Machine Learning with R' by Brett Lantz
Show steps
Gain a deeper understanding of machine learning concepts and their application in R.
View
Machine Learning with R - Fourth Edition: Learn...
on Amazon
Show steps
Show steps
Show steps
Compile a list of resources on machine learning with Google Cloud ML
Show steps
Organize and expand your knowledge base by creating a curated collection of resources on Google Cloud ML.
Show steps
Show steps
Show steps
- Start by creating a new document.
- Add a title to your document.
- Include a brief introduction to Google Cloud ML.
- List resources such as tutorials, documentation, and blog posts.
- Organize your resources into categories.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice partitioning datasets
Show steps
Practice partitioning datasets into training and test sets to build models and evaluate their accuracy.
Show steps
Show steps
Show steps
- Start by creating two empty lists, one for the training set and one for the test set.
- Use a random number generator to select a percentage of the data points to be included in the training set.
- Add the selected data points to the training set.
- Add the remaining data points to the test set.
- Repeat steps 2-4 until both the training and test sets contain the desired number of data points.
Attend a machine learning workshop
Show steps
Expand your knowledge and skills by interacting with experts and peers in a workshop setting.
Show steps
Show steps
Show steps
- Find a machine learning workshop that interests you.
- Register for the workshop.
- Attend the workshop.
- Participate in discussions and activities.
- Network with other attendees.
Participate in a machine learning study group
Show steps
Collaborate with peers to enhance your understanding and reinforce concepts through discussions and problem-solving.
Show steps
Show steps
Show steps
- Find a study group that interests you.
- Join the study group.
- Attend study group meetings.
- Participate in discussions.
- Work together to solve problems.
Build a machine learning model using Google Cloud ML
Show steps
Put your knowledge into practice by building a real-world machine learning model.
Show steps
Show steps
Show steps
- Start by defining the problem you want to solve.
- Gather data relevant to your problem.
- Clean and prepare your data.
- Train a machine learning model using Google Cloud ML.
- Evaluate the performance of your model.
Contribute to an open-source machine learning project
Show steps
Make a meaningful contribution to the machine learning community by contributing to open-source projects.
Show steps
Show steps
Show steps
- Find an open-source machine learning project that interests you.
- Create a GitHub account.
- Fork the project to your own GitHub account.
- Make changes to the project.
- Submit a pull request to the original project.
Career center
Learners who complete Distributed Machine Learning with Google Cloud ML will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and maintaining machine learning models. This course can help you develop the skills needed to be a successful Machine Learning Engineer by providing a foundation in the basics of machine learning, including data preparation, model training, and model evaluation. This course also provides hands-on experience with Google Cloud ML, a platform that can be used to build and deploy machine learning models.
Data Scientist
Data Scientist
Data Scientists use machine learning and other statistical techniques to analyze data and extract insights. This course can help you develop the skills needed to be a successful Data Scientist by providing a foundation in the basics of machine learning, including data preparation, model training, and model evaluation. This course also provides hands-on experience with Google Cloud ML, a platform that can be used to build and deploy machine learning models.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course can help you develop the skills needed to be a successful Software Engineer by providing a foundation in the basics of machine learning. Machine learning is increasingly being used in software applications to improve performance and functionality. This course can help you learn how to use machine learning in your own software applications.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data. This course can help you develop the skills needed to be a successful Quantitative Analyst by providing a foundation in the basics of machine learning. Machine learning is increasingly being used in financial analysis to improve accuracy and efficiency. This course can help you learn how to use machine learning in your own financial analysis.
Business Analyst
Business Analyst
Business Analysts use data and analysis to help businesses make better decisions. This course can help you develop the skills needed to be a successful Business Analyst by providing a foundation in the basics of machine learning. Machine learning is increasingly being used in business analysis to improve accuracy and efficiency. This course can help you learn how to use machine learning in your own business analysis.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course can help you develop the skills needed to be a successful Product Manager by providing a foundation in the basics of machine learning. Machine learning is increasingly being used in product development to improve performance and functionality. This course can help you learn how to use machine learning in your own product development.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for the development and execution of marketing campaigns. This course can help you develop the skills needed to be a successful Marketing Manager by providing a foundation in the basics of machine learning. Machine learning is increasingly being used in marketing to improve targeting and effectiveness. This course can help you learn how to use machine learning in your own marketing campaigns.
Sales Manager
Sales Manager
Sales Managers are responsible for the development and execution of sales strategies. This course can help you develop the skills needed to be a successful Sales Manager by providing a foundation in the basics of machine learning. Machine learning is increasingly being used in sales to improve targeting and effectiveness. This course can help you learn how to use machine learning in your own sales strategies.
Customer Success Manager
Customer Success Manager
Customer Success Managers are responsible for ensuring that customers are satisfied with their products and services. This course can help you develop the skills needed to be a successful Customer Success Manager by providing a foundation in the basics of machine learning. Machine learning is increasingly being used in customer success to improve customer satisfaction and retention. This course can help you learn how to use machine learning in your own customer success strategies.
Technical Writer
Technical Writer
Technical Writers create documentation for software and other technical products. This course can help you develop the skills needed to be a successful Technical Writer by providing a foundation in the basics of machine learning. Machine learning is increasingly being used in technical documentation to improve accuracy and clarity. This course can help you learn how to use machine learning in your own technical writing.
User Experience Designer
User Experience Designer
User Experience Designers design the user interface for software and other products. This course can help you develop the skills needed to be a successful User Experience Designer by providing a foundation in the basics of machine learning. Machine learning is increasingly being used in user experience design to improve usability and satisfaction. This course can help you learn how to use machine learning in your own user experience design.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to help businesses make better decisions. This course can help you develop the skills needed to be a successful Data Analyst by providing a foundation in the basics of machine learning. Machine learning is increasingly being used in data analysis to improve accuracy and efficiency. This course can help you learn how to use machine learning in your own data analysis.
Project Manager
Project Manager
Project Managers plan and execute projects. This course can help you develop the skills needed to be a successful Project Manager by providing a foundation in the basics of machine learning. Machine learning is increasingly being used in project management to improve efficiency and effectiveness. This course can help you learn how to use machine learning in your own project management.
Operations Manager
Operations Manager
Operations Managers oversee the day-to-day operations of a business. This course can help you develop the skills needed to be a successful Operations Manager by providing a foundation in the basics of machine learning. Machine learning is increasingly being used in operations management to improve efficiency and effectiveness. This course can help you learn how to use machine learning in your own operations management.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to help businesses make better decisions. This course can help you develop the skills needed to be a successful Financial Analyst by providing a foundation in the basics of machine learning. Machine learning is increasingly being used in financial analysis to improve accuracy and efficiency. This course can help you learn how to use machine learning in your own financial analysis.
For more career information including salaries, visit:
OpenCourser.com/course/w9jo6d/distributed
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
Distributed Machine Learning with Google Cloud ML.
Save
This comprehensive textbook covers the latest advances in deep learning. It valuable resource for anyone who wants to understand the theory and practice of deep learning.
Save
This classic textbook provides a comprehensive overview of machine learning. It valuable resource for anyone who wants to understand the foundations of machine learning.
Provides a comprehensive overview of distributed machine learning. It covers topics such as data parallelism, model parallelism, and distributed optimization.
Provides a comprehensive overview of machine learning theory and practice. It valuable resource for anyone who wants to understand the theoretical foundations of machine learning.
Provides a comprehensive overview of pattern recognition and machine learning. It valuable resource for anyone who wants to understand the fundamentals of pattern recognition and machine learning.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It valuable resource for anyone who wants to understand the theoretical foundations of machine learning.
Collection of recipes for solving common machine learning problems in Python. It valuable resource for anyone who wants to learn how to use Python for machine learning.
Provides a practical guide to machine learning for predictive data analytics. It valuable resource for anyone who wants to learn how to build and deploy machine learning models for predictive analytics.
Save
Provides a comprehensive overview of data mining. It valuable resource for anyone who wants to learn about the techniques and applications of data mining.
This practical guide shows you how to build and deploy machine learning models using Python libraries such as Scikit-Learn, Keras, and TensorFlow.
Practical guide to machine learning. It valuable resource for anyone who wants to learn how to build and deploy machine learning models.
Provides a comprehensive overview of statistical learning. It valuable resource for anyone who wants to understand the fundamentals of statistical learning.
Save
Gentle introduction to machine learning. It valuable resource for anyone who wants to learn the basics of machine learning without getting bogged down in technical details.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/w9jo6d/distributed
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Effort
1 hour 30 minutes
Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Good to know
Suitable for learners from any background, this course offers a comprehensive introduction to distributed machine learning with Google Cloud ML. Gain valuable hands-on experience with no prior knowledge required
Practical and engaging, this course provides hands-on experience in the Google Cloud console, allowing learners to apply their understanding directly
Ideal for learners seeking to build their skills in data science and machine learning on the Google Cloud platform
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