Demonstrate your proficiency in using TensorFlow to solve deep learning and ML problems. Get recognized for your skills and join our Certificate Network.
TensorFlow Developer Certificate program overview
The goal of this certificate is to provide everyone in the world the opportunity to showcase their expertise in ML in an increasingly AI-driven global job market. This certificate in TensorFlow development is intended as a foundational certificate for students, developers, and data scientists who want to demonstrate practical machine learning skills through the building and training of models using TensorFlow.
The program consists of an assessment exam developed by the TensorFlow team. Developers who pass the exam can join our Certificate Network and display their certificate and badges on their resume, GitHub, and social media platforms including LinkedIn, making it easy to share their level of TensorFlow expertise with the world.
Stay tuned as we are working to add certificate programs for more advanced and specialized TensorFlow practitioners. Check back soon for more information.
Before you take the exam, please review our Candidate Handbook.
Who is the TensorFlow Certificate for?
This level one certificate exam tests a developers foundational knowledge of integrating machine learning into tools and applications. The certificate program requires an understanding of building TensorFlow models using Computer Vision, Convolutional Neural Networks, Natural Language Processing, and real-world image data and strategies.
In order to successfully take the exam, test takers should be comfortable with:
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Foundational principles of ML and Deep Learning
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Building ML models in TensorFlow 2.x
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Building image recognition, object detection, text recognition algorithms with deep neural networks and convolutional neural networks
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Using real-world images in different shapes and sizes to visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy
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Exploring strategies to prevent overfitting, including augmentation and dropouts
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Applying neural networks to solve natural language processing problems using TensorFlow
Benefits
We believe strongly in widening access to people of diverse backgrounds, experiences, geographies, and perspectives to transform the way machine learning and its applications evolve. We're excited to offer a limited number of stipends for the educational material and/or the exam cost in order to achieve this.
Find TensorFlow Certificate holders who have passed the exam to help you with your machine learning and deep learning tasks.
If you don't have the background above, take the DeepLearning.AI TensorFlow Developer Professional Certificate specialization on Coursera or the Intro to TensorFlow for Deep Learning course on Udacity to prepare for the exam. These courses require:
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Introductory Python programming skills
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Prior machine learning or deep learning knowledge is helpful, but not required
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A mathematical background in linear algebra, probability, statistics and calculus is helpful, but not required
Not there yet? Other resources are available to get you up-to-speed.
How it works
Review our Candidate Handbook covering exam criteria and FAQs. Optional: Take the DeepLearning.AI TensorFlow Developer Professional Certificate. This is strongly recommended in order to prepare for the exam.
Register for the exam. Log in with a Gmail Account (if you don't have one, you can create one during the login process), upload your picture ID (such as a driver's license or passport), and provide payment information.
Take and submit the exam. Sign in and take the exam within 6 months of your exam purchase date at any time. You will have a maximum of five hours to complete the exam.
Receive your TensorFlow Certificate. After you have submitted your exam it will be graded, and you will be able to review the status of your submission on your Candidate Portal within 24 hours.
Share your expertise with your community. You can add the certificate and badge to your resume and public profiles, including GitHub, LinkedIn, Twitter, and join our Certificate Network to help recruiters find ML professionals like you.