Here's a compiled list of best & Free Tensorflow certification Courses & Tutorials Online from top universities and top-rated instructors.
Learn Tensorflow -Click on the below Online education provider name to jump their Tensorflow online certification courses.
★ 4.7 (306+ ratings) | 4,774+ students | 50.5 hours on-demand video | Certificate of completion.
Learn to pass Google's official TensorFlow Developer Certificate exam (and add it to your resume), Understand how to integrate Machine Learning into tools and applications.
Complete access to ALL interactive notebooks and ALL course slides as downloadable guides.
Increase your skills in Machine Learning and Deep Learning, to test your abilities with the TensorFlow assessment exam.
★ 4.5 (15,904+ ratings) | 87,873+ students | 14 hours on-demand video | Certificate of completion.
Learn how to use Google's Deep Learning Framework - TensorFlow with Python! Solve problems with cutting edge techniques.
Understand how Neural Networks Work, Use TensorFlow for Classification and Regression Tasks, Build your own Neural Network from Scratch with Python.
Learn how to conduct Reinforcement Learning with OpenAI Gym, Create Generative Adversarial Networks with TensorFlow.
★ 4.6 (5,252+ ratings) | 28,564+ students | 21.5 hours on-demand video | Certificate of completion.
Neural Networks for Computer Vision, Time Series Forecasting, NLP, GANs, Reinforcement Learning, and More.
Learn how to build a Deep Reinforcement Learning Stock Trading Bot, Convolutional Neural Networks (CNNs), Computer Vision, Image Recognition.
Build Amazing Applications of Deep Learning and Artificial Intelligence in TensorFlow 2.0. Learn how to use Tensorflow 2.0 in Data Science.
How to implement Artificial Neural Networks in Tensorflow 2.0, How to implement Recurrent Neural Networks in Tensorflow 2.0, How to build Machine Learning Pipeline in Tensorflow 2.0.
Learn to use Python for Deep Learning with Google's latest Tensorflow 2 library and Keras. Learn to use TensorFlow 2.0 for Deep Learning.
Perform Image Classification with Convolutional Neural Networks. Forecast Time Series data with Recurrent Neural Networks, Use Deep Learning for medical imaging.
★ 4.7 (16,080+ ratings) | 118,620+ students | Approximately 4 months to complete | Certificate of completion.
Best practices for TensorFlow, a popular open-source machine learning framework to train a neural network for a computer vision applications.
Build natural language processing systems using TensorFlow.
Handle real-world image data and explore strategies to prevent overfitting, including augmentation and dropout.
★ 4.8 (501+ ratings) | 7,412+ students | Approximately 5 months to complete | Certificate of completion.
Expand your skill set and master TensorFlow. Customize your machine learning models through four hands-on courses.
Understand the underlying basis of the Functional API and build exotic non-sequential model types, custom loss functions, and layers, Practice object detection, image segmentation, and visual interpretation of convolutions.
This Specialization is intended for machine learning researchers and practitioners who are seeking to develop practical skills in the popular deep learning framework TensorFlow.
You will acquire practical skills in developing deep learning models for a range of applications such as image classification, language translation, and text and image generation.
In this course you will learn a complete end-to-end workflow for developing deep learning models with Tensorflow, from building, training, evaluating and predicting with models using the Sequential API, validating your models and including regularisation, implementing callbacks, and saving and loading models.
You will learn how to develop probabilistic models with TensorFlow, making particular use of the TensorFlow Probability library, which is designed to make it easy to combine probabilistic models with deep learning.
★ Expert instruction | Self-paced | 8 months.
Fundamental concepts of Deep Learning, including various Neural Networks for supervised and unsupervised learning.
Build, train, and deploy different types of Deep Architectures, including Convolutional Networks, Recurrent Networks, and Autoencoders.
Application of Deep Learning to real-world scenarios such as object recognition and Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other types of classifiers.
Learn how to apply Deep Learning with TensorFlow to this type of data to solve real-world problems. Explain foundational TensorFlow concepts such as the main functions, operations and the execution pipelines.
Describe how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions.
Learn how deep learning algorithms can be used to solve important engineering problems. Justify the development state-of-the-art deep learning algorithms.
Implement, optimize and tune state-of-the-art deep neural network architectures. Identify and address the security aspects of state-of-the-art deep learning algorithms.
Learn to program in TensorFlow Lite for microcontrollers so that you can write the code, and deploy your model to your very own tiny microcontroller. Before you know it, you’ll be implementing an entire TinyML application.
An understanding of the hardware of a microcontroller-based device, How to program your own TinyML device, How to train a microcontroller-based device.
★ 3,050 members like this course | 54,265 learners | 1h 46m of content | Certificate of completion.
In this course, learn how to install TensorFlow and use it to build a simple deep learning model.
How to get TensorFlow up and running, instructor Adam Geitgey demonstrates how to create and train a machine learning model, as well as how to leverage visualization tools to analyze and improve your model.
In this course, instructor Harshit Tyagi provides a complete guide to understanding NLP using recurrent neural networks (RNNs).
Describes the important concept of word embeddings and shows you how to use TensorFlow to classify movie reviews and project vectors.
In this course, instructor Matt Scarpino helps to acquaint you with this exciting tool. Here, he explores the process of developing TensorFlow applications and running them on the Google Cloud Machine Learning (ML) Engine.
This course introduces you to ML basics, and demonstrates how to set up and use TensorFlow to train a model and generate live results.
How to work with different tensor types, variables, models, and layers; how to import a project and explore datasets.
Learn foundational machine learning algorithms, starting with data cleaning and supervised models. Then, move on to exploring deep and unsupervised learning. At each step, get practical experience by applying your skills to code exercises and projects.
Learn how to deploy deep learning models on mobile and embedded devices with TensorFlow Lite.
Learn how to build deep learning applications with TensorFlow. This course was developed by the TensorFlow team and Udacity as a practical approach to deep learning for software developers.