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Dhiraj Kumar
In this course, TensorFlow Developer Certificate - TensorFlow Developer Skills , you will learn several items.
First, you will learn how to compile and run Python programs in PyCharm.
Next, you will discover how to find information about TensorFlow APIs.
Then, how to solve error messages fro
m the TensorFlow API.
Finally, how to save ML models and check the model file size
Read more
In this course, TensorFlow Developer Certificate - TensorFlow Developer Skills , you will learn several items. First, you will learn how to compile and run Python programs in PyCharm. Next, you will discover how to find information about TensorFlow APIs. Then, how to solve error messages fro m the TensorFlow API. Finally, how to save ML models and check the model file size
Read more
In this course, TensorFlow Developer Certificate - TensorFlow Developer Skills , you will learn several items. First, you will learn how to compile and run Python programs in PyCharm. Next, you will discover how to find information about TensorFlow APIs. Then, how to solve error messages fro m the TensorFlow API. Finally, how to save ML models and check the model file size
In this course, TensorFlow Developer Certificate - TensorFlow Developer Skills , you will lear n several items. First, you will learn how to program in Python, resolve Python issues, and compile and run Python programs in PyCharm. Next is how to find information about TensorFlow APIs, including guides and API references on tensorflow.org. Then, know how to debug, investigate, and solve error messages from the TensorFlow API. Then, how to search beyond tensorflow.org, as and when necessary, to solve your TensorFlow questions. Then how to create ML models using TensorFlow where the model size is reason able for solving the problem. Then how to save ML models and check the model file size. Finally, Understand the compatibility discrepancies between different versions of TensorFlow
Register for this course and see more details by visiting:
OpenCourser.com/course/fsyjsp/tensorflow
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What's inside
Syllabus
Course Overview
Python Programming with PyCharm
Explore TensorFlow API
Create ML model Using TensorFlow
Read more
Syllabus
Course Overview
Python Programming with PyCharm
Explore TensorFlow API
Create ML model Using TensorFlow
Read more
TensorFlow Version Compatibility
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides foundational level skills in building ML models with TensorFlow
Covers essential aspects of TensorFlow, including API exploration and debugging
Guided by Dhiraj Kumar, an experienced instructor in TensorFlow development
May require prior programming experience for optimal comprehension
Utilizes PyCharm as the development environment, which may be unfamiliar to some learners
Save this course
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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 TensorFlow Developer Certificate - TensorFlow Developer Skills with these
activities:
Organize Course Notes and Resources
Show steps
Establish a structured system for storing and organizing course materials for easy reference.
Show steps
-
Create a dedicated folder or notebook for course materials.
-
Categorize and label notes, assignments, and other resources.
-
Use digital tools or apps for note-taking and organization.
Review Python Fundamentals
Show steps
Refresh your understanding of Python concepts before starting the course.
Browse courses on
Python
Show steps
-
Review online tutorials or documentation on Python basics.
-
Complete practice exercises or quizzes to test your knowledge.
Read 'Hands-On Machine Learning with TensorFlow 2.0'
Show steps
Supplement your knowledge with a comprehensive guide to machine learning using TensorFlow.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
-
Read selected chapters relevant to the course topics.
-
Work through the practical exercises and examples provided in the book.
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Connect with TensorFlow experts
Show steps
Seek guidance from experienced TensorFlow developers to enhance your learning.
Show steps
-
Identify potential mentors
-
Reach out to mentors
-
Schedule regular meetings
Consolidate course materials
Show steps
Bring together and organize lecture notes, lab materials, and practice problems to reinforce the concepts covered in this course.
Show steps
-
Gather course materials
-
Organize materials into a central location
-
Review and highlight key concepts
Practice Python Exercises
Show steps
Reinforce your understanding of Python basics through repetitive exercises.
Browse courses on
Python
Show steps
-
Complete the Python Tutorial on the official website.
-
Solve coding challenges on platforms like LeetCode or HackerRank.
Participate in TensorFlow study groups
Show steps
Collaborate with peers to discuss TensorFlow concepts, troubleshoot challenges, and reinforce learning.
Show steps
-
Find a study group
-
Attend group meetings regularly
-
Contribute to discussions and ask questions
Explore TensorFlow tutorials
Show steps
Deepen your understanding of TensorFlow by working through guided tutorials that cover specific aspects and applications of the library.
Browse courses on
TensorFlow
Show steps
-
Identify relevant tutorials
-
Follow the instructions
-
Apply what you learn to practical examples
Explore TensorFlow API Documentations
Show steps
Familiarize yourself with the TensorFlow API documentation to find information and resources.
Show steps
-
Review the official TensorFlow API reference guide.
-
Read through tutorials and guides on TensorFlow's website.
-
Utilize the TensorFlow documentation search engine to explore specific topics and functions.
Explore TensorFlow GitHub Resources
Show steps
Expand your knowledge by exploring TensorFlow-related projects and discussions on GitHub.
Browse courses on
TensorFlow
Show steps
-
Review repositories and documentation for TensorFlow.
-
Participate in discussions or ask questions on the TensorFlow GitHub community.
Attend TensorFlow workshops
Show steps
Expand your knowledge and skills by attending TensorFlow workshops that cover advanced topics and hands-on exercises.
Show steps
-
Find relevant workshops
-
Attend workshops
-
Apply what you learn to your own projects
Solve coding challenges
Show steps
Test your skills and knowledge by solving coding challenges that focus on implementing TensorFlow concepts.
Show steps
-
Find coding challenges
-
Attempt to solve the challenges
-
Review solutions and identify areas for improvement
Create Custom Models Using TensorFlow
Show steps
Enhance your practical skills by building and training custom ML models using TensorFlow.
Browse courses on
TensorFlow
Show steps
-
Design and implement a model architecture for a specific problem.
-
Train and evaluate the model using appropriate datasets.
-
Optimize the model's performance by adjusting hyperparameters.
Build a TensorFlow project
Show steps
Apply your TensorFlow knowledge to solve a practical problem by designing, implementing, and evaluating a real-world project.
Show steps
-
Brainstorm project ideas
-
Design the project architecture
-
Implement the project using TensorFlow
-
Evaluate the project's performance
Develop a Project Proposal for an ML Model
Show steps
Solidify your understanding by proposing and outlining a machine learning project using TensorFlow.
Browse courses on
Machine Learning Projects
Show steps
-
Identify a problem or use case for machine learning.
-
Define the scope and goals of your project.
-
Outline the methodology and approach you will use.
Organize Course Notes and Resources
Show steps
Establish a structured system for storing and organizing course materials for easy reference.
Show steps
Show steps
Show steps
- Create a dedicated folder or notebook for course materials.
- Categorize and label notes, assignments, and other resources.
- Use digital tools or apps for note-taking and organization.
Review Python Fundamentals
Show steps
Refresh your understanding of Python concepts before starting the course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review online tutorials or documentation on Python basics.
- Complete practice exercises or quizzes to test your knowledge.
Read 'Hands-On Machine Learning with TensorFlow 2.0'
Show steps
Supplement your knowledge with a comprehensive guide to machine learning using TensorFlow.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
Show steps
Show steps
- Read selected chapters relevant to the course topics.
- Work through the practical exercises and examples provided in the book.
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Connect with TensorFlow experts
Show steps
Seek guidance from experienced TensorFlow developers to enhance your learning.
Show steps
Show steps
Show steps
- Identify potential mentors
- Reach out to mentors
- Schedule regular meetings
Consolidate course materials
Show steps
Bring together and organize lecture notes, lab materials, and practice problems to reinforce the concepts covered in this course.
Show steps
Show steps
Show steps
- Gather course materials
- Organize materials into a central location
- Review and highlight key concepts
Practice Python Exercises
Show steps
Reinforce your understanding of Python basics through repetitive exercises.
Browse courses on
Python
Show steps
Show steps
Show steps
- Complete the Python Tutorial on the official website.
- Solve coding challenges on platforms like LeetCode or HackerRank.
Participate in TensorFlow study groups
Show steps
Collaborate with peers to discuss TensorFlow concepts, troubleshoot challenges, and reinforce learning.
Show steps
Show steps
Show steps
- Find a study group
- Attend group meetings regularly
- Contribute to discussions and ask questions
Explore TensorFlow tutorials
Show steps
Deepen your understanding of TensorFlow by working through guided tutorials that cover specific aspects and applications of the library.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Identify relevant tutorials
- Follow the instructions
- Apply what you learn to practical examples
Explore TensorFlow API Documentations
Show steps
Familiarize yourself with the TensorFlow API documentation to find information and resources.
Show steps
Show steps
Show steps
- Review the official TensorFlow API reference guide.
- Read through tutorials and guides on TensorFlow's website.
- Utilize the TensorFlow documentation search engine to explore specific topics and functions.
Explore TensorFlow GitHub Resources
Show steps
Expand your knowledge by exploring TensorFlow-related projects and discussions on GitHub.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Review repositories and documentation for TensorFlow.
- Participate in discussions or ask questions on the TensorFlow GitHub community.
Attend TensorFlow workshops
Show steps
Expand your knowledge and skills by attending TensorFlow workshops that cover advanced topics and hands-on exercises.
Show steps
Show steps
Show steps
- Find relevant workshops
- Attend workshops
- Apply what you learn to your own projects
Solve coding challenges
Show steps
Test your skills and knowledge by solving coding challenges that focus on implementing TensorFlow concepts.
Show steps
Show steps
Show steps
- Find coding challenges
- Attempt to solve the challenges
- Review solutions and identify areas for improvement
Create Custom Models Using TensorFlow
Show steps
Enhance your practical skills by building and training custom ML models using TensorFlow.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Design and implement a model architecture for a specific problem.
- Train and evaluate the model using appropriate datasets.
- Optimize the model's performance by adjusting hyperparameters.
Build a TensorFlow project
Show steps
Apply your TensorFlow knowledge to solve a practical problem by designing, implementing, and evaluating a real-world project.
Show steps
Show steps
Show steps
- Brainstorm project ideas
- Design the project architecture
- Implement the project using TensorFlow
- Evaluate the project's performance
Develop a Project Proposal for an ML Model
Show steps
Solidify your understanding by proposing and outlining a machine learning project using TensorFlow.
Browse courses on
Machine Learning Projects
Show steps
Show steps
Show steps
- Identify a problem or use case for machine learning.
- Define the scope and goals of your project.
- Outline the methodology and approach you will use.
Career center
Learners who complete TensorFlow Developer Certificate - TensorFlow Developer Skills will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs and develops machine learning models. This course directly addresses the skills necessary for this role. It covers topics such as creating ML models using TensorFlow, debugging and resolving API errors, and checking model file size.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. This course's focus on Python programming and TensorFlow API can provide a great foundation for aspiring Software Engineers. Understanding how to compile and run Python programs, find and resolve API errors, as well as create and save ML models are all essential skills in this field.
Data Scientist
Data Scientist
A Data Scientist uses data to solve problems and make predictions. This course provides a strong foundation for aspiring Data Scientists by teaching them how to use TensorFlow API, create ML models, and check their file size. These skills are essential for working with large datasets and building data-driven solutions.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
An Artificial Intelligence Engineer designs and develops AI systems. This course provides a useful introduction to the field by covering topics such as Python programming, TensorFlow API, and ML model creation. These skills are essential for building and deploying AI solutions.
Computer Scientist
Computer Scientist
A Computer Scientist researches and designs new computing technologies. This course provides a solid foundation for aspiring Computer Scientists by teaching them the fundamentals of Python programming, TensorFlow API, and ML model creation.
Software Developer
Software Developer
A Software Developer designs, develops, and maintains software systems. This course can be helpful for aspiring Software Developers by providing them with a foundation in Python programming, TensorFlow API, and ML model creation.
Product Manager
Product Manager
A Product Manager manages the development and launch of new products. This course may be useful for aspiring Product Managers by providing them with a basic understanding of Python programming, TensorFlow API, and ML model creation.
Data Engineer
Data Engineer
A Data Engineer designs and builds data pipelines. This course may be useful for aspiring Data Engineers by providing them with a basic understanding of Python programming, TensorFlow API, and ML model creation.
Project Manager
Project Manager
A Project Manager plans and executes projects. This course may be useful for aspiring Project Managers by providing them with a basic understanding of Python programming, TensorFlow API, and ML model creation.
Sales Analyst
Sales Analyst
A Sales Analyst uses data to identify sales opportunities. This course may be useful for aspiring Sales Analysts by providing them with a basic understanding of Python programming, TensorFlow API, and ML model creation.
Business Analyst
Business Analyst
A Business Analyst uses data to solve business problems. This course may be useful for aspiring Business Analysts by providing them with a basic understanding of Python programming, TensorFlow API, and ML model creation.
Data Analyst
Data Analyst
A Data Analyst uses data to identify trends and patterns. This course may be useful for aspiring Data Analysts by providing them with a basic understanding of Python programming, TensorFlow API, and ML model creation.
Financial Analyst
Financial Analyst
A Financial Analyst uses data to make investment decisions. This course may be useful for aspiring Financial Analysts by providing them with a basic understanding of Python programming, TensorFlow API, and ML model creation.
Marketing Analyst
Marketing Analyst
A Marketing Analyst uses data to understand customer behavior. This course may be useful for aspiring Marketing Analysts by providing them with a basic understanding of Python programming, TensorFlow API, and ML model creation.
Operations Analyst
Operations Analyst
An Operations Analyst uses data to improve business operations. This course may be useful for aspiring Operations Analysts by providing them with a basic understanding of Python programming, TensorFlow API, and ML model creation.
For more career information including salaries, visit:
OpenCourser.com/course/fsyjsp/tensorflow
Reading list
We've selected nine 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 Developer Certificate - TensorFlow Developer Skills.
Comprehensive guide to machine learning with TensorFlow 2.0, covering a wide range of topics from data preprocessing and model selection to model evaluation and deployment. It provides clear explanations and practical examples to help readers understand and apply machine learning concepts.
Provides a practical introduction to deep learning, covering the fundamentals of neural networks, convolutional neural networks, and recurrent neural networks. It offers a hands-on approach, with code examples and exercises to help readers implement deep learning models.
Provides a comprehensive overview of machine learning in Python, covering a wide range of topics from data preprocessing and model selection to model evaluation and deployment. It offers a hands-on approach, with code examples and exercises to reinforce learning.
Save
Provides a comprehensive overview of deep learning, covering the fundamentals of neural networks, convolutional neural networks, and recurrent neural networks. It offers a theoretical and practical approach to deep learning, making it a valuable resource for researchers and practitioners.
Save
Provides a comprehensive overview of machine learning, covering a wide range of topics from data preprocessing and model selection to model evaluation and deployment. It offers a theoretical and practical approach to machine learning, making it a valuable resource for researchers and practitioners.
Provides a comprehensive overview of data science in Python, covering a wide range of topics from data preprocessing and model selection to model evaluation and deployment. It offers a hands-on approach, with code examples and exercises to reinforce learning.
Provides a comprehensive overview of statistical learning, covering a wide range of topics from data preprocessing and model selection to model evaluation and deployment. It offers a theoretical and practical approach to statistical learning, making it a valuable resource for researchers and practitioners.
Provides a comprehensive overview of machine learning in R, covering a wide range of topics from data preprocessing and model selection to model evaluation and deployment. It offers a hands-on approach, with code examples and exercises to reinforce learning.
Provides a comprehensive overview of deep learning in Java, covering a wide range of topics from data preprocessing and model selection to model evaluation and deployment. It offers a hands-on approach, with code examples and exercises to reinforce learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/fsyjsp/tensorflow
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Time
88 hours
Level
Introductory
Via
Pluralsight
Instructor
Dhiraj Kumar
Language
English
Good to know
Provides foundational level skills in building ML models with TensorFlow
Covers essential aspects of TensorFlow, including API exploration and debugging
Guided by Dhiraj Kumar, an experienced instructor in TensorFlow development
May require prior programming experience for optimal comprehension
Utilizes PyCharm as the development environment, which may be unfamiliar to some learners
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