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Google Cloud Training
Este curso aborda o design e a criação de um pipeline de dados de entrada com o TensorFlow 2.x, além de vários aspectos relacionados aos modelos de ML, por exemplo: como desenvolver esses modelos com o TensorFlow 2.x e o Keras, como melhorar a precisão deles; como criá-los para uso em escala e como desenvolver modelos de ML especializados.
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OpenCourser.com/course/uxx90n/tensorflow
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What's inside
Syllabus
Introdução ao curso
Este módulo apresenta uma visão geral do curso e dos objetivos a serem alcançados.
Introdução ao ecossistema do TensorFlow
Este módulo apresenta o framework do TensorFlow e dá uma prévia dos principais componentes da plataforma, bem como a hierarquia geral da API.
Read more
Syllabus
Introdução ao curso
Este módulo apresenta uma visão geral do curso e dos objetivos a serem alcançados.
Introdução ao ecossistema do TensorFlow
Este módulo apresenta o framework do TensorFlow e dá uma prévia dos principais componentes da plataforma, bem como a hierarquia geral da API.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Covers designing and implementing an ingestion data pipeline with TensorFlow 2.x
Helps learners develop ML models with TensorFlow 2.x and Keras
Provides guidance on how to create production-ready ML models
Covers how to develop specialized ML models
Taught by Google's cloud experts
Requires learners to come in with some experience using TensorFlow
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Create your own learning path.
Save this course to your list so you can find it easily later.
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Reviews summary
Tensorflow on google cloud fundamentals
According to learners, this course provides an excellent foundation for using TensorFlow 2.x on Google Cloud. Students frequently praise the hands-on labs and step-by-step guidance, finding them highly practical for bridging ML development and cloud deployment. Its coverage of Vertex AI and data pipelines with tf.data is considered a key strength. While explanations are generally clear and well-structured, some learners noted a lack of depth for advanced topics and a potential assumption of prior ML knowledge.
Benefits those with some prior machine learning knowledge.
"My main critique is that it assumes some prior knowledge of machine learning concepts."
"A stronger pre-requisite check or a quick refresher would be beneficial."
"I think it's best suited for those with some existing ML background but new to GCP."
Strong coverage of TensorFlow 2.x, Keras, and the valuable Vertex AI.
"The course's coverage of Vertex AI was especially valuable as it's a key tool now."
"Very useful for understanding data pipelines and Keras on GCP."
"This course made it easy to understand how TensorFlow integrates with GCP services."
Provides clear explanations and a solid foundation for ML on Google Cloud.
"This course provided an excellent foundation for using TensorFlow 2.x on Google Cloud."
"The concepts were explained clearly, and the step-by-step guidance was perfect."
"I gained a solid foundation from completing this course; instructors explain everything very clearly."
The course excels with its hands-on exercises for real-world application.
"The hands-on labs using Vertex AI were incredibly practical."
"I especially appreciated the sections on data pipelines and model deployment."
"The practical exercises were a strong point, really useful for understanding data pipelines."
Minor points on slide detail and potential outdated references.
"The Portuguese audio was fine, but the slides could be more detailed."
"Felt a bit outdated in some references too."
"The labs were functional but could use more complex scenarios."
Some topics could benefit from more advanced and in-depth explanations.
"I felt some parts could go deeper into advanced topics, especially model optimization."
"The course covers the basics but doesn't dive deep enough for practical implementation."
"I struggled with some of the more complex parts because the background theory wasn't sufficiently explained."
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 TensorFlow on Google Cloud - Português Brasileiro with these
activities:
Review Python
Show steps
Refreshes foundational Python knowledge to support success in this course
Browse courses on
Python
Show steps
-
Review documentation
-
Solve practice problems
-
Build a simple program
Review Machine Learning Concepts
Show steps
Refreshes foundational machine learning concepts to support success in this course
Browse courses on
Machine Learning
Show steps
-
Review documentation
-
Solve practice problems
-
Build a simple machine learning model
Solve TensorFlow Practice Problems
Show steps
Strengthens problem-solving skills specific to TensorFlow
Show steps
-
Find practice problems
-
Solve problems
-
Review solutions
Two other activities
Expand to see all activities and additional details
Show all five activities
Follow TensorFlow Tutorials
Show steps
Provides additional guidance and reinforcement of TensorFlow concepts
Show steps
-
Find relevant tutorials
-
Follow the tutorials
-
Apply what you learn
Build a TensorFlow Project
Show steps
Applies TensorFlow concepts to a real-world problem, fostering deeper understanding
Show steps
-
Identify a project idea
-
Gather data
-
Build and train a model
-
Deploy the project
Review Python
Show steps
Refreshes foundational Python knowledge to support success in this course
Browse courses on
Python
Show steps
Show steps
Show steps
- Review documentation
- Solve practice problems
- Build a simple program
Review Machine Learning Concepts
Show steps
Refreshes foundational machine learning concepts to support success in this course
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Review documentation
- Solve practice problems
- Build a simple machine learning model
Solve TensorFlow Practice Problems
Show steps
Strengthens problem-solving skills specific to TensorFlow
Show steps
Show steps
Show steps
- Find practice problems
- Solve problems
- Review solutions
Two other activities
Expand to see all activities and additional details
Show all five activities
Follow TensorFlow Tutorials
Show steps
Provides additional guidance and reinforcement of TensorFlow concepts
Show steps
Show steps
Show steps
- Find relevant tutorials
- Follow the tutorials
- Apply what you learn
Build a TensorFlow Project
Show steps
Applies TensorFlow concepts to a real-world problem, fostering deeper understanding
Show steps
Show steps
Show steps
- Identify a project idea
- Gather data
- Build and train a model
- Deploy the project
Career center
Learners who complete TensorFlow on Google Cloud - Português Brasileiro will develop knowledge and skills
that may be useful to these careers:
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They use their knowledge of programming languages, data structures, and algorithms to create software that meets the needs of users. This course may be useful for aspiring Software Engineers with an interest in machine learning, as it will give them hands-on experience in building and deploying machine learning models. Machine learning is an increasingly important part of modern software systems, and this course will give students the skills needed to work with it effectively.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are experts in building and implementing machine learning models to solve real-world problems. Their responsibilities include designing, developing, and deploying machine learning systems, as well as monitoring and maintaining these systems over time. This course may be useful for someone seeking to become a Machine Learning Engineer because it will teach the fundamentals of machine learning and give practical experience in building machine learning models. The course covers topics such as data preprocessing, model training, and model evaluation.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines. They work with data scientists and other stakeholders to ensure that data is clean, accurate, and accessible. This course may be useful for aspiring Data Engineers because it will teach the fundamentals of machine learning, as well as how to apply these techniques to data engineering. The course covers topics such as data pipelines, data quality, and data security.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment recommendations. They analyze financial statements, conduct industry research, and build financial models to help investors make informed decisions. This course may be useful for aspiring Financial Analysts because it will teach the fundamentals of machine learning, as well as how to apply these techniques to financial data. The course covers topics such as financial modeling, portfolio management, and risk assessment.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts (Quants) use mathematical and statistical models to analyze financial data and make investment decisions. They are responsible for developing and implementing trading strategies, as well as managing risk. This course may be useful for aspiring Quants because it will teach the fundamentals of machine learning, as well as how to apply these techniques to financial data. The course covers topics such as time series analysis, risk management, and portfolio optimization.
Data Scientist
Data Scientist
Data Scientists use machine learning to translate raw data into valuable business decisions. Their duties commonly include gathering and analyzing data, building machine learning models, and presenting insights to stakeholders. This course may be useful for an aspiring Data Scientist because it will teach the fundamentals of building and applying machine learning models. While this course is not specific to data science, it will teach techniques that are commonly used by data scientists.
Risk Manager
Risk Manager
Risk Managers identify, assess, and manage risks. They work with organizations to develop and implement strategies to mitigate risks and protect assets. This course may be useful for aspiring Risk Managers because it will teach the fundamentals of machine learning, as well as how to apply these techniques to risk management. The course covers topics such as risk identification, risk assessment, and risk mitigation.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to help organizations make informed decisions. They use statistical and machine learning techniques to identify trends and patterns in data, and they communicate their findings to stakeholders in a clear and concise way. This course may be useful for someone seeking to become a Data Analyst because it will teach the fundamentals of machine learning, as well as how to preprocess and analyze data. The course also covers topics such as data visualization and communication, which are important skills for Data Analysts.
Solutions Architect
Solutions Architect
Solutions Architects design and implement technology solutions for organizations. They work with clients to understand their business needs and develop solutions that meet those needs. This course may be useful for aspiring Solutions Architects because it will teach the fundamentals of machine learning, as well as how to apply these techniques to solution architecture. The course covers topics such as cloud computing, big data, and artificial intelligence.
Market Research Analyst
Market Research Analyst
Market Research Analysts collect, analyze, and interpret market data to help organizations make informed decisions. They use statistical and machine learning techniques to identify trends and patterns in data, and they communicate their findings to stakeholders in a clear and concise way. This course may be useful for someone seeking to become a Market Research Analyst because it will teach the fundamentals of machine learning, as well as how to gather and analyze data. The course also covers topics such as data visualization and communication.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve complex business problems. They work with organizations to improve efficiency, productivity, and profitability. This course may be useful for aspiring Operations Research Analysts because it will teach the fundamentals of machine learning, as well as how to apply these techniques to operations research problems. The course covers topics such as linear programming, optimization, and simulation.
DevOps Engineer
DevOps Engineer
DevOps Engineers work to bridge the gap between development and operations teams. They are responsible for building, deploying, and maintaining software systems. This course may be useful for aspiring DevOps Engineers because it will teach the fundamentals of machine learning, as well as how to apply these techniques to DevOps. The course covers topics such as continuous integration, continuous delivery, and infrastructure automation.
Business Analyst
Business Analyst
Business Analysts use data to help organizations improve their performance. They work with stakeholders to identify business needs, gather and analyze data, and make recommendations for improvement. This course may be useful for aspiring Business Analysts because it will teach the fundamentals of machine learning, as well as how to apply these techniques to business problems. The course covers topics such as data mining, predictive analytics, and decision making.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to ensure that products meet the needs of users. This course may be useful for aspiring Product Managers because it will teach the fundamentals of machine learning, as well as how to apply these techniques to product development. The course covers topics such as user research, market analysis, and product planning.
Project Manager
Project Manager
Project Managers are responsible for planning, executing, and closing projects. They work with stakeholders to define project scope, develop project plans, and manage project risks. This course may be useful for aspiring Project Managers because it will teach the fundamentals of machine learning, as well as how to apply these techniques to project management. The course covers topics such as project planning, risk management, and stakeholder management.
For more career information including salaries, visit:
OpenCourser.com/course/uxx90n/tensorflow
Reading list
We've selected eight 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
TensorFlow on Google Cloud - Português Brasileiro.
Serving as an excellent resource for building practical machine learning models with TensorFlow 2.0, this book offers comprehensive coverage of concepts like data preprocessing, neural networks, and deep learning. Suitable for both beginners and experienced practitioners, it provides practical insights and hands-on examples to accelerate your machine learning development.
Offers a comprehensive guide to machine learning using Python, covering both theoretical concepts and practical applications. It provides a solid foundation for building and deploying machine learning models.
Provides a comprehensive overview of TensorFlow, covering its architecture, core concepts, and applications in machine learning and deep learning. Suitable for beginners and intermediate learners, it offers a solid foundation for building and deploying machine learning models using TensorFlow.
Provides a practical introduction to TensorFlow for deep learning applications. It covers the fundamental concepts, architectures, and techniques used in deep learning, enabling you to build and train deep learning models with TensorFlow.
Offers a comprehensive overview of TensorFlow, covering its architecture, core concepts, and applications in machine learning and deep learning. Suitable for beginners and intermediate learners, it provides a solid foundation for building and deploying machine learning models using TensorFlow.
Provides a comprehensive guide to TensorFlow for machine learning applications. It covers the fundamental concepts, techniques, and real-world examples to help you build and deploy machine learning models with TensorFlow.
Offers a comprehensive introduction to TensorFlow for machine learning applications. Covering the fundamental concepts and techniques, it provides a practical guide to building and deploying machine learning models with TensorFlow.
Provides a practical and intuitive introduction to the principles and applications of deep learning. Written by the creator of the Keras API, this book empowers you to build, train, and deploy deep learning models with ease. It offers a comprehensive guide to the theory and implementation of deep learning algorithms, making it a valuable resource for both practitioners and researchers.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/uxx90n/tensorflow
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Effort
1 week of study, 6-8 hours/week
Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Transcript
Português
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