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Google Cloud Training
Machine Learning is one of the most innovative fields in technology, and the Google Cloud Platform has been instrumental in furthering its development. With a host of APIs, Google Cloud has a tool for just about any machine learning job. In this advanced-level Google Cloud Labs Series, you will get hands-on practice with machine learning at scale and how to employ the advanced machine learning infrastructure available on Google Cloud.
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/lqyumr/advanced
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Delves into cloud-based machine learning, which is gaining popularity in industry
Provides hands-on experience in machine learning at scale, a valuable skill for data scientists
Covers Google Cloud Platform's advanced machine learning infrastructure, which is crucial for handling large-scale ML projects
Taught by expert instructors from Google Cloud Training, ensuring high-quality content and industry insights
Timed access to the online environment may be a constraint for some learners with busy schedules
Save this course
Save Advanced Machine Learning: Machine Learning Infrastructure to your list so you can find it easily later:
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Reviews summary
Unreliable infrastructure
Students of 'Advanced Machine Learning: Machine Learning Infrastructure' have reported that the third lab in the course has been broken for months and that there is no support from Qwiklabs. As a result, it is currently impossible to complete the course and obtain a certificate. Several students have reported that labs are this course are the most useful in the specialization, but ultimately, the whole specialization is handicapped by the broken lab.
Students indicate that the labs are useful and prefer them over others in the specialization.
"However it is useful for hands on experience."
"Also the labs in this course are the most useful ones in the specialization in my opinion."
Students report that it's impossible to complete due to a broken lab with no support from Qwiklabs.
"One lab is broken for months and no one can complete the course because of that."
"Warning, can't be completed since there is a missing lab."
Students report that the labs are unreliable and frequently broken.
"The Labs are broken and there is no support from qwiklabs."
"Warning, can't be completed since there is a missing lab."
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 Advanced Machine Learning: Machine Learning Infrastructure with these
activities:
Organize and review course materials
Show steps
This activity will help you stay organized and on track throughout the course, ensuring you have a comprehensive understanding of the materials.
Show steps
-
Create a system for organizing notes, assignments, and other course materials
-
Review and summarize key concepts from each lecture or module
Review linear algebra and calculus
Show steps
Strengthen your mathematical foundation by reviewing linear algebra and calculus.
Browse courses on
Linear Algebra
Show steps
-
Identify the key concepts of linear algebra and calculus that are relevant to machine learning
-
Review online resources or textbooks to refresh your understanding
-
Solve practice problems to test your comprehension
Review core concepts of machine learning
Show steps
Reinforce your foundational understanding of key machine learning concepts.
Browse courses on
Machine Learning
Show steps
-
Revisit textbooks or online materials on machine learning basics
-
Review lecture notes from previous courses or tutorials
-
Solve practice problems or coding challenges to test your understanding
16 other activities
Expand to see all activities and additional details
Show all 19 activities
Review key concepts from prerequisites
Show steps
Reinforces foundational knowledge in machine learning and Google Cloud APIs to provide a stronger base for learning.
Browse courses on
Machine Learning
Show steps
-
Review documentation on Google Cloud Machine Learning Engine.
-
Complete a tutorial on using a key machine learning API.
Review basic machine learning concepts
Show steps
This activity will help you refresh your understanding of the fundamental concepts of machine learning, ensuring you have a strong foundation for the course.
Show steps
-
Review articles and tutorials on basic machine learning concepts
-
Complete practice exercises or quizzes on basic machine learning algorithms
Seek guidance from experienced machine learning professionals
Show steps
This activity will connect you with experts in the field, providing you with valuable insights and support throughout your learning journey.
Show steps
-
Identify potential mentors who align with your interests
-
Reach out to them and request guidance
Participate in online discussion forums on machine learning
Show steps
Facilitates knowledge sharing, problem-solving, and peer support in exploring machine learning concepts and techniques.
Show steps
-
Join online forums and discussion groups related to machine learning.
-
Actively participate in discussions, share knowledge, and ask questions.
Walkthrough Google Cloud's machine learning tutorials
Show steps
Build a strong foundation by following Google Cloud's machine learning tutorials.
Browse courses on
Machine Learning
Show steps
-
Explore the machine learning tutorials offered by Google Cloud
-
Choose a tutorial that aligns with your skill level and interests
-
Follow the tutorial step-by-step, taking notes and experimenting with the code
Follow Google Cloud Machine Learning tutorials
Show steps
Enhance your practical skills by following hands-on tutorials provided by Google Cloud.
Browse courses on
Google Cloud Platform
Show steps
-
Choose a tutorial relevant to your learning goals
-
Follow the instructions step-by-step
-
Experiment with different parameters and settings
-
Review the results and troubleshoot any issues
Mentor junior machine learning learners
Show steps
This activity will not only reinforce your understanding but also enhance your communication and teaching skills while contributing to the community.
Show steps
-
Identify opportunities to mentor others
-
Provide guidance and support to junior learners
-
Answer their questions and provide feedback
Follow tutorials on Google Cloud Machine Learning Engine use cases
Show steps
Provides practical experience with Google Cloud Machine Learning Engine and showcases its capabilities in real-world scenarios.
Show steps
-
Identify use cases for machine learning in your domain.
-
Find and complete tutorials that align with these use cases.
-
Implement the solutions from the tutorials in your own projects.
Practice implementing machine learning algorithms
Show steps
This activity will provide you with hands-on experience in implementing machine learning algorithms, solidifying your understanding and improving your coding skills.
Browse courses on
Machine Learning Algorithms
Show steps
-
Choose a machine learning algorithm and implement it from scratch using a programming language
-
Solve coding challenges or practice problems involving machine learning algorithms
Solve machine learning practice problems
Show steps
Sharpen your machine learning skills by solving practice problems.
Browse courses on
Machine Learning Algorithms
Show steps
-
Identify a platform or resource that offers machine learning practice problems
-
Select a set of problems that cover different machine learning concepts
-
Solve the problems using your knowledge of machine learning algorithms and techniques
Practice coding machine learning algorithms
Show steps
Develop proficiency in implementing machine learning models through coding exercises.
Browse courses on
Python
Show steps
-
Identify a practice platform or website
-
Select coding challenges or exercises
-
Implement machine learning algorithms from scratch
-
Analyze and optimize your code
Develop a machine learning model to predict customer churn
Show steps
Apply your machine learning skills to a real-world problem by developing a model to predict customer churn.
Browse courses on
Customer Churn Prediction
Show steps
-
Gather a dataset of customer data
-
Explore the data and identify potential features for your model
-
Train and evaluate different machine learning models
-
Deploy your model and monitor its performance
Practice coding machine learning algorithms
Show steps
Strengthens coding skills and deepens understanding of machine learning algorithms.
Show steps
-
Identify common machine learning algorithms.
-
Implement these algorithms in your preferred programming language.
-
Solve coding challenges related to machine learning.
Follow tutorials on advanced machine learning techniques
Show steps
This activity will expose you to advanced machine learning techniques and best practices, enhancing your knowledge and skills in the field.
Show steps
-
Identify advanced machine learning techniques relevant to your interests
-
Find and follow tutorials or online courses on these techniques
-
Implement and experiment with the techniques in practice
Develop a machine learning portfolio project
Show steps
Showcase your machine learning abilities by creating a portfolio project.
Browse courses on
Machine Learning Projects
Show steps
-
Choose a real-world problem that you can solve using machine learning
-
Develop a plan for your project, including data collection, model selection, and evaluation metrics
-
Implement your project using the Google Cloud Platform
-
Write a report or documentation that describes your project and its results
Participate in machine learning competitions
Show steps
This activity will provide you with a platform to test your skills against others, gain feedback, and stay updated with the latest trends in machine learning.
Show steps
-
Find and register for machine learning competitions
-
Build and submit your models
-
Analyze the results and learn from them
Organize and review course materials
Show steps
This activity will help you stay organized and on track throughout the course, ensuring you have a comprehensive understanding of the materials.
Show steps
Show steps
Show steps
- Create a system for organizing notes, assignments, and other course materials
- Review and summarize key concepts from each lecture or module
Review linear algebra and calculus
Show steps
Strengthen your mathematical foundation by reviewing linear algebra and calculus.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Identify the key concepts of linear algebra and calculus that are relevant to machine learning
- Review online resources or textbooks to refresh your understanding
- Solve practice problems to test your comprehension
Review core concepts of machine learning
Show steps
Reinforce your foundational understanding of key machine learning concepts.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Revisit textbooks or online materials on machine learning basics
- Review lecture notes from previous courses or tutorials
- Solve practice problems or coding challenges to test your understanding
16 other activities
Expand to see all activities and additional details
Show all 19 activities
Review key concepts from prerequisites
Show steps
Reinforces foundational knowledge in machine learning and Google Cloud APIs to provide a stronger base for learning.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Review documentation on Google Cloud Machine Learning Engine.
- Complete a tutorial on using a key machine learning API.
Review basic machine learning concepts
Show steps
This activity will help you refresh your understanding of the fundamental concepts of machine learning, ensuring you have a strong foundation for the course.
Show steps
Show steps
Show steps
- Review articles and tutorials on basic machine learning concepts
- Complete practice exercises or quizzes on basic machine learning algorithms
Seek guidance from experienced machine learning professionals
Show steps
This activity will connect you with experts in the field, providing you with valuable insights and support throughout your learning journey.
Show steps
Show steps
Show steps
- Identify potential mentors who align with your interests
- Reach out to them and request guidance
Participate in online discussion forums on machine learning
Show steps
Facilitates knowledge sharing, problem-solving, and peer support in exploring machine learning concepts and techniques.
Show steps
Show steps
Show steps
- Join online forums and discussion groups related to machine learning.
- Actively participate in discussions, share knowledge, and ask questions.
Walkthrough Google Cloud's machine learning tutorials
Show steps
Build a strong foundation by following Google Cloud's machine learning tutorials.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Explore the machine learning tutorials offered by Google Cloud
- Choose a tutorial that aligns with your skill level and interests
- Follow the tutorial step-by-step, taking notes and experimenting with the code
Follow Google Cloud Machine Learning tutorials
Show steps
Enhance your practical skills by following hands-on tutorials provided by Google Cloud.
Browse courses on
Google Cloud Platform
Show steps
Show steps
Show steps
- Choose a tutorial relevant to your learning goals
- Follow the instructions step-by-step
- Experiment with different parameters and settings
- Review the results and troubleshoot any issues
Mentor junior machine learning learners
Show steps
This activity will not only reinforce your understanding but also enhance your communication and teaching skills while contributing to the community.
Show steps
Show steps
Show steps
- Identify opportunities to mentor others
- Provide guidance and support to junior learners
- Answer their questions and provide feedback
Follow tutorials on Google Cloud Machine Learning Engine use cases
Show steps
Provides practical experience with Google Cloud Machine Learning Engine and showcases its capabilities in real-world scenarios.
Show steps
Show steps
Show steps
- Identify use cases for machine learning in your domain.
- Find and complete tutorials that align with these use cases.
- Implement the solutions from the tutorials in your own projects.
Practice implementing machine learning algorithms
Show steps
This activity will provide you with hands-on experience in implementing machine learning algorithms, solidifying your understanding and improving your coding skills.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Choose a machine learning algorithm and implement it from scratch using a programming language
- Solve coding challenges or practice problems involving machine learning algorithms
Solve machine learning practice problems
Show steps
Sharpen your machine learning skills by solving practice problems.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Identify a platform or resource that offers machine learning practice problems
- Select a set of problems that cover different machine learning concepts
- Solve the problems using your knowledge of machine learning algorithms and techniques
Practice coding machine learning algorithms
Show steps
Develop proficiency in implementing machine learning models through coding exercises.
Browse courses on
Python
Show steps
Show steps
Show steps
- Identify a practice platform or website
- Select coding challenges or exercises
- Implement machine learning algorithms from scratch
- Analyze and optimize your code
Develop a machine learning model to predict customer churn
Show steps
Apply your machine learning skills to a real-world problem by developing a model to predict customer churn.
Browse courses on
Customer Churn Prediction
Show steps
Show steps
Show steps
- Gather a dataset of customer data
- Explore the data and identify potential features for your model
- Train and evaluate different machine learning models
- Deploy your model and monitor its performance
Practice coding machine learning algorithms
Show steps
Strengthens coding skills and deepens understanding of machine learning algorithms.
Show steps
Show steps
Show steps
- Identify common machine learning algorithms.
- Implement these algorithms in your preferred programming language.
- Solve coding challenges related to machine learning.
Follow tutorials on advanced machine learning techniques
Show steps
This activity will expose you to advanced machine learning techniques and best practices, enhancing your knowledge and skills in the field.
Show steps
Show steps
Show steps
- Identify advanced machine learning techniques relevant to your interests
- Find and follow tutorials or online courses on these techniques
- Implement and experiment with the techniques in practice
Develop a machine learning portfolio project
Show steps
Showcase your machine learning abilities by creating a portfolio project.
Browse courses on
Machine Learning Projects
Show steps
Show steps
Show steps
- Choose a real-world problem that you can solve using machine learning
- Develop a plan for your project, including data collection, model selection, and evaluation metrics
- Implement your project using the Google Cloud Platform
- Write a report or documentation that describes your project and its results
Participate in machine learning competitions
Show steps
This activity will provide you with a platform to test your skills against others, gain feedback, and stay updated with the latest trends in machine learning.
Show steps
Show steps
Show steps
- Find and register for machine learning competitions
- Build and submit your models
- Analyze the results and learn from them
Career center
Learners who complete Advanced Machine Learning: Machine Learning Infrastructure will develop knowledge and skills
that may be useful to these careers:
Machine Learning Operations Engineer
Machine Learning Operations Engineer
This course is tailored for individuals pursuing a career as Machine Learning Operations Engineers. It covers advanced machine learning infrastructure on Google Cloud, focusing on deploying and managing machine learning models at scale. By taking this course, learners will gain practical experience and enhance their skills in monitoring, maintaining, and optimizing ML models in production, which is essential for MLOps Engineers.
Data Engineer
Data Engineer
Data Engineers play a crucial role in building and maintaining data pipelines for machine learning. This course is designed to provide Data Engineers with hands-on experience in managing machine learning infrastructure on Google Cloud. By understanding how to deploy and manage ML models at scale, Data Engineers can enhance their skills and become more effective in supporting data science teams.
Cloud Developer
Cloud Developer
This course is well-suited for professionals seeking to become Cloud Developers. It introduces learners to the advanced machine learning infrastructure on Google Cloud and provides practice in managing machine learning projects and deploying models at scale. This hands-on experience will help learners build a solid foundation for a successful career as Cloud Developers specializing in machine learning.
Data Architect
Data Architect
Data Architects are responsible for designing and managing data systems and infrastructure. This course is well-suited for professionals seeking such a role, as it provides hands-on experience with the advanced machine learning infrastructure on Google Cloud. By gaining expertise in managing and deploying machine learning models at scale, learners can enhance their skills and become more competitive in the field.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers focus on developing and implementing AI solutions. This course is relevant to this career path as it provides hands-on experience with machine learning infrastructure on Google Cloud. The course covers advanced techniques for deploying and managing machine learning models, which is essential for AI Engineers to develop and maintain AI systems.
Machine Learning Researcher
Machine Learning Researcher
Individuals pursuing a career as Machine Learning Researchers may find this course beneficial. It provides hands-on experience with the Google Cloud Platform and its machine learning tools, allowing researchers to explore advanced techniques and algorithms in a real-world setting. The course covers topics such as model deployment and management, which are essential for researchers to bring their findings into production.
Cloud Architect
Cloud Architect
This course is well-suited for professionals looking to become Cloud Architects due to its emphasis on machine learning infrastructure on Google Cloud. Architects are responsible for designing and managing cloud systems, and this course provides hands-on experience with deploying and managing machine learning models at scale. This knowledge will help learners gain a competitive edge in the field.
Big Data Engineer
Big Data Engineer
For individuals pursuing a career as Big Data Engineers, this course may be useful. It introduces learners to the Google Cloud Platform and its tools for handling large amounts of data for machine learning. The course emphasizes deploying and managing machine learning models at scale, which is essential for Big Data Engineers working with complex data pipelines.
Software Engineer
Software Engineer
For individuals interested in Software Engineering, taking this course may be helpful for gaining expertise in machine learning infrastructure. The course introduces learners to a host of APIs for machine learning jobs, providing experience in integrating machine learning models into software systems. Software Engineers can benefit from a deep understanding of machine learning principles and infrastructure.
Quantitative Analyst
Quantitative Analyst
For individuals pursuing a career as Quantitative Analysts, this course provides valuable insights and practical experience. It covers advanced machine learning infrastructure on Google Cloud, emphasizing the deployment and management of machine learning models at scale. By gaining hands-on experience with these techniques, Quantitative Analysts can expand their skillset and become more proficient in applying machine learning to financial data analysis and modeling.
Data Scientist
Data Scientist
Machine learning and data science are closely related fields, and this course may also be useful for aspiring Data Scientists. It introduces learners to the advanced machine learning infrastructure on Google Cloud and provides practice in using a host of APIs for machine learning tasks. As Data Scientists rely on infrastructure for experimentation and production, this course may be helpful for gaining experience and advancing in this subfield.
DevOps Engineer
DevOps Engineer
This course may be useful for professionals seeking a career as DevOps Engineers, particularly those with an interest in deploying and managing machine learning models. DevOps Engineers focus on automating and streamlining software development and infrastructure management, and this course will provide hands-on experience with using the advanced machine learning infrastructure on Google Cloud.
Machine Learning Engineer
Machine Learning Engineer
This course may be useful for individuals seeking to become Machine Learning Engineers. Those who take this course will be introduced to the Google Cloud Platform (GCP) and how to use its machine learning tools and infrastructure to manage machine learning projects and deploy models at scale. This hands-on experience will help learners build a foundation for a successful career in machine learning engineering.
Product Manager
Product Manager
This course can be beneficial for Product Managers who want to gain a deeper understanding of machine learning infrastructure. With the increasing adoption of machine learning in various products, it has become important for Product Managers to have a strong foundation in its underlying technology. By understanding how to deploy and manage ML models at scale, Product Managers can make informed decisions and drive successful product development in the rapidly evolving field of machine learning.
Business Analyst
Business Analyst
For professionals looking to advance their career as Business Analysts, this course may be useful. It provides an introduction to machine learning infrastructure on Google Cloud and its applications in business. By gaining an understanding of machine learning tools and techniques, Business Analysts can enhance their analytical skills and contribute more effectively to data-driven decision-making.
For more career information including salaries, visit:
OpenCourser.com/course/lqyumr/advanced
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
Advanced Machine Learning: Machine Learning Infrastructure.
Save
Comprehensive guide to deep learning, covering both the theoretical foundations and practical applications. It valuable resource for both beginners and experienced practitioners.
Provides a practical introduction to machine learning using Python. It covers the basics of machine learning, as well as more advanced topics such as deep learning.
Provides a comprehensive overview of machine learning, covering both the theoretical foundations and practical applications. It valuable resource for both beginners and experienced practitioners.
Provides a comprehensive overview of statistical learning, covering both the theoretical foundations and practical applications. It valuable resource for both beginners and experienced practitioners.
Provides a comprehensive overview of pattern recognition and machine learning, covering both the theoretical foundations and practical applications. It valuable resource for both beginners and experienced practitioners.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It valuable resource for both beginners and experienced practitioners.
Provides a practical introduction to machine learning for people with no prior experience. It valuable resource for beginners who want to learn about machine learning quickly and easily.
Provides a comprehensive overview of machine learning, covering both the theoretical foundations and practical applications. It valuable resource for both beginners and experienced practitioners.
Provides a practical introduction to machine learning using Python. It covers the basics of machine learning, as well as more advanced topics such as deep learning.
Save
Provides a practical introduction to machine learning for business professionals. It covers the basics of machine learning, as well as more advanced topics such as deep learning.
Provides a very basic introduction to machine learning. It valuable resource for people who have no prior experience with machine learning.
Provides a comprehensive overview of machine learning, covering both the theoretical foundations and practical applications. It valuable resource for both beginners and experienced practitioners.
Provides a very basic introduction to machine learning. It valuable resource for people who have no prior experience with machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/lqyumr/advanced
Share
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Time
5 hours
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
This course belongs to a
collection
called
Hands-on Machine Learning with Google Cloud Labs. It's comprised of three courses:
- Intermediate Machine Learning: TensorFlow on Google Cloud
- Advanced Machine Learning: Machine Learning Infrastructure (viewing)
- Introduction to Machine Learning: Language Processing
Good to know
Delves into cloud-based machine learning, which is gaining popularity in industry
Provides hands-on experience in machine learning at scale, a valuable skill for data scientists
Covers Google Cloud Platform's advanced machine learning infrastructure, which is crucial for handling large-scale ML projects
Taught by expert instructors from Google Cloud Training, ensuring high-quality content and industry insights
Timed access to the online environment may be a constraint for some learners with busy schedules
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