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TensorFlow is a library for is an open-source software library for high-performance numerical computation that's great for writing models that can train and run on platforms ranging from your laptop to a fleet of servers in the Cloud to an edge device. This...
Read more
TensorFlow is a library for is an open-source software library for high-performance numerical computation that's great for writing models that can train and run on platforms ranging from your laptop to a fleet of servers in the Cloud to an edge device. This...
Read more
TensorFlow is a library for is an open-source software library for high-performance numerical computation that's great for writing models that can train and run on platforms ranging from your laptop to a fleet of servers in the Cloud to an edge device. This Google Cloud Labs Series takes you beyond the basics of using predefined models and teaches you how to build, train and deploy your own 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/cjhdix/intermediate
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Good to know
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Know what's good ,
what to watch for , and
possible dealbreakers
Builds a foundation in machine learning for learners with some familiarity with TensorFlow
Suitable for individuals aspiring to enhance their machine learning skills within the Google Cloud ecosystem
May benefit data scientists, machine learning engineers, and developers who seek to implement machine learning models on Google Cloud
Assumes familiarity with some machine learning concepts
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Save Intermediate Machine Learning: TensorFlow on Google Cloud to your list so you can find it easily later:
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Reviews summary
Problematic machine learning course
The lone review for this TensorFlow course is decidedly negative. The reviewer complains that the course material required for certification is unavailable and doesn't work.
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 Intermediate Machine Learning: TensorFlow on Google Cloud with these
activities:
TensorFlow Essentials Review
Show steps
Review the core concepts of TensorFlow and numerical computation to strengthen your foundational understanding.
Browse courses on
TensorFlow
Show steps
-
Review TensorFlow documentation and tutorials
-
Create a simple TensorFlow project to practice
Connect with TensorFlow Experts
Show steps
Seek guidance and support from experienced TensorFlow professionals to accelerate your learning and enhance your skills.
Browse courses on
TensorFlow
Show steps
-
Identify potential mentors in the TensorFlow community
-
Reach out to mentors and request guidance
TensorFlow Resource Compilation
Show steps
Organize and expand your notes, assignments, and other course materials to create a comprehensive resource for future reference.
Browse courses on
TensorFlow
Show steps
-
Gather all relevant course materials
-
Organize and categorize materials
-
Create a digital or physical reference
Five other activities
Expand to see all activities and additional details
Show all eight activities
TensorFlow Exercises
Show steps
Engage in hands-on exercises to reinforce your understanding of TensorFlow's capabilities and apply it to real-world scenarios.
Browse courses on
TensorFlow
Show steps
-
Solve TensorFlow exercises from online platforms
-
Build and train a neural network for a specific task
TensorFlow Workshop Attendance
Show steps
Participate in TensorFlow workshops to learn from experts, network with peers, and gain practical insights.
Browse courses on
TensorFlow
Show steps
-
Identify and register for relevant TensorFlow workshops
-
Attend workshops and actively participate
Contribute to TensorFlow Community
Show steps
Engage in open-source projects related to TensorFlow to contribute to its development and expand your understanding of its ecosystem.
Browse courses on
TensorFlow
Show steps
-
Identify open-source TensorFlow projects
-
Contribute to projects by reporting bugs, providing documentation, or writing code
Advanced TensorFlow Techniques
Show steps
Explore advanced TensorFlow techniques to expand your knowledge and unlock its full potential.
Browse courses on
TensorFlow
Show steps
-
Follow video tutorials on advanced TensorFlow topics
-
Implement a complex TensorFlow architecture
TensorFlow Project Development
Show steps
Develop a substantial TensorFlow project to showcase your skills and deepen your understanding through practical application.
Browse courses on
TensorFlow
Show steps
-
Define a real-world problem to solve with TensorFlow
-
Design and implement a TensorFlow solution
-
Evaluate and refine your project
TensorFlow Essentials Review
Show steps
Review the core concepts of TensorFlow and numerical computation to strengthen your foundational understanding.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Review TensorFlow documentation and tutorials
- Create a simple TensorFlow project to practice
Connect with TensorFlow Experts
Show steps
Seek guidance and support from experienced TensorFlow professionals to accelerate your learning and enhance your skills.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Identify potential mentors in the TensorFlow community
- Reach out to mentors and request guidance
TensorFlow Resource Compilation
Show steps
Organize and expand your notes, assignments, and other course materials to create a comprehensive resource for future reference.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Gather all relevant course materials
- Organize and categorize materials
- Create a digital or physical reference
Five other activities
Expand to see all activities and additional details
Show all eight activities
TensorFlow Exercises
Show steps
Engage in hands-on exercises to reinforce your understanding of TensorFlow's capabilities and apply it to real-world scenarios.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Solve TensorFlow exercises from online platforms
- Build and train a neural network for a specific task
TensorFlow Workshop Attendance
Show steps
Participate in TensorFlow workshops to learn from experts, network with peers, and gain practical insights.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Identify and register for relevant TensorFlow workshops
- Attend workshops and actively participate
Contribute to TensorFlow Community
Show steps
Engage in open-source projects related to TensorFlow to contribute to its development and expand your understanding of its ecosystem.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Identify open-source TensorFlow projects
- Contribute to projects by reporting bugs, providing documentation, or writing code
Advanced TensorFlow Techniques
Show steps
Explore advanced TensorFlow techniques to expand your knowledge and unlock its full potential.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Follow video tutorials on advanced TensorFlow topics
- Implement a complex TensorFlow architecture
TensorFlow Project Development
Show steps
Develop a substantial TensorFlow project to showcase your skills and deepen your understanding through practical application.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Define a real-world problem to solve with TensorFlow
- Design and implement a TensorFlow solution
- Evaluate and refine your project
Career center
Learners who complete Intermediate Machine Learning: TensorFlow on Google Cloud will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use machine learning algorithms to extract insights from data. They work in a variety of industries, including finance, healthcare, and manufacturing. The “Intermediate Machine Learning: TensorFlow on Google Cloud” course can provide you with the necessary skills to become a successful Data Scientist. This course will teach you how to use TensorFlow to build, train, and deploy machine learning models on Google Cloud.
Machine Learning Engineer
Machine Learning Engineer
As a Machine Learning Engineer you will be responsible for the collection, analysis, and management of large datasets. You will work closely with data scientists to build and deploy models to solve a range of business problems. TensorFlow is an open-source library that can be used to build high-performance numerical computation. The “Intermediate Machine Learning: TensorFlow on Google Cloud” course can provide you with the necessary skills to become a successful Machine Learning Engineer. This course goes beyond the basics of using predefined models and teaches you how to build, train, and deploy your own on Google Cloud.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work in a variety of industries, including finance, healthcare, and manufacturing. The “Intermediate Machine Learning: TensorFlow on Google Cloud” course can provide you with the necessary skills to become a successful Software Engineer. This course will teach you how to use TensorFlow to build, train, and deploy machine learning models on Google Cloud.
Machine Learning Architect
Machine Learning Architect
Machine Learning Architects design and implement machine learning solutions. They work in a variety of industries, including finance, healthcare, and manufacturing. The “Intermediate Machine Learning: TensorFlow on Google Cloud” course can provide you with the necessary skills to become a successful Machine Learning Architect. This course will teach you how to use TensorFlow to build, train, and deploy machine learning models on Google Cloud.
Product Manager
Product Manager
Product Managers develop and manage products. They work in a variety of industries, including finance, healthcare, and manufacturing. The “Intermediate Machine Learning: TensorFlow on Google Cloud” course may be useful to Product Managers who want to learn how to use TensorFlow to build, train, and deploy machine learning models on Google Cloud.
Data Engineer
Data Engineer
Data Engineers design and build data pipelines to collect, clean, and store data. They work in a variety of industries, including finance, healthcare, and manufacturing. The “Intermediate Machine Learning: TensorFlow on Google Cloud” course may be useful to Data Engineers who want to learn how to use TensorFlow to build, train, and deploy machine learning models on Google Cloud.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They work in a variety of industries, including finance, healthcare, and manufacturing. The “Intermediate Machine Learning: TensorFlow on Google Cloud” course may be useful to Quantitative Analysts who want to learn how to use TensorFlow to build, train, and deploy machine learning models on Google Cloud.
Research Analyst
Research Analyst
Research Analysts conduct research and provide insights to clients. They work in a variety of industries, including finance, healthcare, and manufacturing. The “Intermediate Machine Learning: TensorFlow on Google Cloud” course may be useful to Research Analysts who want to learn how to use TensorFlow to build, train, and deploy machine learning models on Google Cloud.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. They work in a variety of industries, including finance, healthcare, and manufacturing. The “Intermediate Machine Learning: TensorFlow on Google Cloud” course may be useful to Data Analysts who want to learn how to use TensorFlow to build, train, and deploy machine learning models on Google Cloud.
Business Analyst
Business Analyst
Business Analysts analyze business needs and develop solutions to improve efficiency and effectiveness. They work in a variety of industries, including finance, healthcare, and manufacturing. The “Intermediate Machine Learning: TensorFlow on Google Cloud” course may be useful to Business Analysts who want to learn how to use TensorFlow to build, train, and deploy machine learning models on Google Cloud.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They work in a variety of industries, including finance, healthcare, and manufacturing. The “Intermediate Machine Learning: TensorFlow on Google Cloud” course may be useful to Statisticians who want to learn how to use TensorFlow to build, train, and deploy machine learning models on Google Cloud.
Project Manager
Project Manager
Project Managers plan, execute, and close projects. They work in a variety of industries, including finance, healthcare, and manufacturing. The “Intermediate Machine Learning: TensorFlow on Google Cloud” course may be useful to Project Managers who want to learn how to use TensorFlow to build, train, and deploy machine learning models on Google Cloud.
For more career information including salaries, visit:
OpenCourser.com/course/cjhdix/intermediate
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
Intermediate Machine Learning: TensorFlow on Google Cloud.
Practical guide to using TensorFlow for machine learning tasks. It covers a wide range of topics, including data preparation, model training, and evaluation.
Provides a comprehensive overview of deep learning, with a focus on using TensorFlow. It covers a wide range of topics, including convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Provides a gentle introduction to machine learning, with a focus on using Python. It covers a wide range of topics, including data preparation, model training, and evaluation.
Provides a comprehensive overview of machine learning, with a focus on using R. It covers a wide range of topics, including data preparation, model training, and evaluation.
Provides a comprehensive overview of statistical learning, with a focus on machine learning. It covers a wide range of topics, including linear regression, logistic regression, and neural networks.
Provides a comprehensive overview of pattern recognition and machine learning. It covers a wide range of topics, including Bayesian inference, support vector machines, and hidden Markov models.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It covers a wide range of topics, including Bayesian inference, Gaussian processes, and Markov chain Monte Carlo.
Save
Provides a comprehensive overview of deep learning. It covers a wide range of topics, including convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Provides a practical guide to using machine learning for hackers. It covers a wide range of topics, including data preparation, model training, and evaluation.
Provides a practical guide to using machine learning in action. It covers a wide range of topics, including data preparation, model training, and evaluation.
Provides a practical guide to using deep learning with Python and Keras. It covers a wide range of topics, including convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Provides a comprehensive overview of advanced deep learning techniques using TensorFlow 2 and Keras.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/cjhdix/intermediate
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Time
6 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 (viewing)
- Advanced Machine Learning: Machine Learning Infrastructure
- Introduction to Machine Learning: Language Processing
Good to know
Builds a foundation in machine learning for learners with some familiarity with TensorFlow
Suitable for individuals aspiring to enhance their machine learning skills within the Google Cloud ecosystem
May benefit data scientists, machine learning engineers, and developers who seek to implement machine learning models on Google Cloud
Assumes familiarity with some machine learning concepts
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