DeepLearning.AI TensorFlow Developer Certificate Online
This DeepLearning AI certificate course lets you dive into the cutting-edge world of AI specialization, machine learning and data-driven solutions.
Franklin University has partnered with Coursera Campus to provide cutting-edge certificates to learners seeking to advance. Courses are open to all learners. No application required.
Included in your subscription
Get unlimited access to over 7,000 offerings found on the Coursera website – including guided projects, specializations and professional certificates offered by hundreds of leading universities and companies. You also get access to all 39 professional certificates found in the Franklin Marketplace.
LEARN MOREWhat You Will Learn
- Learn fundamental concepts and best practices of deep learning using TensorFlow
- Discover advanced ML techniques, such as image recognition and feature extraction to improve models
- Develop practical skills for NLP by learning to process text, tokenize it, then sequence tokens to train a neural network
- Understand time series forecasting by preparing time series data and building a prediction model using real-world data
About the DeepLearning.AI TensorFlow Developer Professional Certificate
The DeepLearning.AI TensorFlow Developer Professional Certificate is designed for IT professionals and non-tech types who are interested in learning about one of the most popular open-source machine learning frameworks: TensorFlow.
The four courses that comprise the DeepLearning.AI TensorFlow Developer Professional Certificate are intended to introduce you to neural networks and teach you the fundamentals of building and training powerful machine learning (ML) models.
Thanks to 16 different Python programming assignments, you'll gain relevant, real-world experience building scalable AI-powered apps with TensorFlow. You'll also use natural language processing systems to teach machines how to analyze human speech and respond appropriately. And you'll work with a variety of image shapes and sizes to learn how computers interpret information along with strategies for optimizing the computer vision model.
When you complete all the courses and projects in this Certificate program, you'll also be prepared to sit for the Google TensorFlow Certificate exam.
So, whether you're an AI practitioner looking to strengthen your skill set or a non-technical pro wanting to get in on the creation of an AI-powered future, this Professional Certificate program might just be the right one for you.
Required DeepLearning.AI TensorFlow Developer Certificate Courses
INTERMEDIATE | Computer Science | Self-paced | 40 hours
If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the DeepLearning.AI TensorFlow Developer Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new DeepLearning.AI TensorFlow Developer Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.
INTERMEDIATE | Data Science | Self-paced | 31 hours
If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the DeepLearning.AI TensorFlow Developer Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In Course 2 of the DeepLearning.AI TensorFlow Developer Specialization, you will learn advanced techniques to improve the computer vision model you built in Course 1. You will explore how to work with real-world images in different shapes and sizes, visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy, and explore strategies to prevent overfitting, including augmentation and dropout. Finally, Course 2 will introduce you to transfer learning and how learned features can be extracted from models. The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.
INTERMEDIATE | Data Science | Self-paced | 46 hours
If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In Course 3 of the DeepLearning.AI TensorFlow Developer Specialization, you will build natural language processing systems using TensorFlow. You will learn to process text, including tokenizing and representing sentences as vectors, so that they can be input to a neural network. You’ll also learn to apply RNNs, GRUs, and LSTMs in TensorFlow. Finally, you’ll get to train an LSTM on existing text to create original poetry! The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new DeepLearning.AI TensorFlow Developer Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.
INTERMEDIATE | Data Science | Self-paced | 42 hours
If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In this fourth course, you will learn how to build time series models in TensorFlow. You’ll first implement best practices to prepare time series data. You’ll also explore how RNNs and 1D ConvNets can be used for prediction. Finally, you’ll apply everything you’ve learned throughout the Specialization to build a sunspot prediction model using real-world data! The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new DeepLearning.AI TensorFlow Developer Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.Complete This Certificate. Get College Credit.
You know that skill-specific courses will open the door to specialized jobs, but did you know that they will also move you closer to a degree at Franklin University?
The University has evaluated hundreds of certifications for industry-recognized proficiencies and awards credit that equates to specific Franklin courses, as well as technical- or elective-credit requirements. See how much time and money you'll save toward your degree by building on prior learning credit.
Browse & Filter
Bolster Your Professional Skills
Take back control or rethink your career by strengthening your skills with a Professional Certificate through Franklin. Learn, hone or master job-related skills with professional development classes that won't break the bank or gobble up your free time. These online courses let you feed your curiosity and develop new skills that have real value in the workplace. Learn at your own pace. Cancel your subscription anytime.
Showcase Your Capabilities
Through Franklin’s partnership with Coursera, Certificate courses let you apply your learnings and build a career portfolio that helps demonstrate your professional capabilities to employers. Whether you're moving into a new field or progressing in your current one, the hands-on projects offer real-world examples that help illustrate your skills and abilities. Project completion is required to earn your Certificate.
Gain a Competitive Advantage
Get noticed by hiring managers and by your network of professional connections when you add a Professional Certificate to your credentials. Many Certificates are step toward full certification while others are the start of a new career journey. At Franklin, your Certificate also may be evaluated for course credit if you decide to enroll in one of our many degree programs.
Frequently Asked Questions
When you enroll in this self-paced certificate program, you decide how quickly you want to complete each of the courses in the specialization. To access the courses, you pay a small monthly cost of $35, so the total cost of your Professional Certificate depends on you. Plus, you can take a break or cancel your subscription anytime.
It takes about 3-4 months to finish all the courses and hands-on projects to earn your certificate.
This intermediate-level series is for technology-minded individuals with related experience, such as software development.
Use it to showcase your neural network skills, power an AI career or take another step toward a Google TensorFlow Certificate.
No. Courses offered through the Marketplace are for all learners. There is no application or admission process.
Please submit your certificate to plc@franklin.edu for review and processing. After your official evaluation has been completed, please review it to ensure that all eligible credits have been applied.
You can submit documentation before or after you apply to Franklin.