Last Updated on November 21, 2019

TensorFlow is a free and end-to-end open source platform that Google created and used to design, build, and train Machine Learning and Deep learning models.

Whether you’re an expert or a beginner, TensorFlow makes it easy develop and train ML models.

TensorFlow has a comprehensive, flexible ecosystem of tools, libraries and extension, and community resources that helps you solve exceedingly challenging, real-world problems with machine learning.

TensorFlow also has a Math library in Python and you can do the numerical computations with data flow graphs.

If you are a curious developer who wants to build scalable AI-powered algorithms, you have to understand how to use the tools to build them to take your project to the next level.

Mathematics is exceedingly important prerequisite for Learning Machine Learning or for taking any Deep Learning Course, so if you need to need strengthen your skills , i’ve got you covered you in this piece about Maths for Machine Learning.

I know the options out there, and what skills are needed for learners. TensorFlow offers enormous potential for artificial neural networks and in this guide, for Best TensorFlow Courses we interrogate what’s possible for you.

Please note, this compilation will be updated at least once every quarter to ensure that the courses are aligned with latest TensorFlow Update. So, go ahead & save this one in your pocket/ bookmarks. ☆

Now, without a further ado, let’s get started.

5 TensorFlow Courses from World-Class Educators

As a beginner, you may be looking for a way to get a solid understanding of TensorFlow that’s not only rigorous and practical, but also concise and fast.

Below, I’ve curated a selection of the best TensorFlow for beginners and experts who aspire to expand their minds.

— Introduction to TensorFlow in Python This course, Introduction to TensorFlow in Python from DataCamp will help you to learn the fundamentals of neural networks. You will understand how to develop, train, and make predictions with the models that have powered major advances in recommendation systems, image classification, and FinTech. You will learn to use both high-level APIs to design and train deep learning models in few lines of code; and also use low-level APIs to move beyond off-the-shelf routines. You will also learn to accurately predict house prices, credit card borrower defaults, and images of sign-language gestures.

Is it right for you?

This course is suitable for learners with moderate understanding of scikit-learn, experience in Python and background in Mathematics.

Upon the successful completion of this course you will have the skills to effectively build deep learning models using TensorFlow.

— Introduction to TensorFlow for Artificial Intelligence, Machine Learning and Deep Learning This course is created by Google Brain and is part of Machine Learning and Deep Learning specialization from Andrew Ng. In this course, you will receive a broad introduction to TensforFlow learning for Artificial Intelligence, Machine Learning, and Deep Learning. This course will give you a new set of tools to open previously unexplored scenarios to equip you from Basics to Mastery of TensorFlow. Is it right for you? This course is also part of Deep Learning Specialization and assumes no prior experience of Machine Learning and Deep Learning. However, intermediate level of knowledge in Python and basic understanding of maths is required. Upon the completion of this course, you will have a deeper understanding of how neural networks work and will equipped to build and apply scalable models to solve real-world problems with TensorFlow. GO TO COURSE

This course shows you how to install and use TensorFlow, and provided in-depth introduction to machine learning and deep learning for building artificial neural networks. This course is Created by Jerry Kurata, Technology Expert and best selling author of Machine Learning and Deep Learning Courses on Pluralsight and Coursera. In this, course, you’ll learn use TensorFlow and create a range of machine learning and deep learning models, from simple linear regression to complex deep neural networks. Is it right for you? This is one of the highly rated course on internet and suitable for learners who are just getting started with TensorFlow from the field ion. Upon the successful completion of this course, you will be equipped to take an approach to effective problem solving and interacting with the results of your work with your peers. GO TO COURSE — Intro to TensorFlow This course is created by Google Cloud Training Instructors to help you get familiar with low-level TensorFlow. You will learn to work your way through the necessary concepts and APIs so as to be able to write Machine Learning and Deep Learning Models. In this course, you’ll be provided with a TensorFlow model to scale out the training of that model and learn the key concepts for offering high-performance predictions using Cloud Machine Learning Engine. Is it right for you? There are no pre-requisites for taking this courses. However, prior experience programming and High-School level Mathematics will help you to get started. Upon the completion of this course, you will be able well equipped to create machine learning models and Build Neural Network in TensorFlow. GO TO COURSE — Creative Applications of Deep Learning with TensorFlow This course, Creative Applications of Deep Learning with TensorFlow introduces learners to deep learning with the state-of-the-art approach to building artificial intelligence algorithms. You will learn the basic components of deep learning, what it means, how it works, and develop code necessary to build various algorithms such as deep convolutional networks, variational auto-encoders, generative adversarial networks, and recurrent neural networks. This course aims to help learners build the necessary components of certain algorithms and understand how to apply them for exploring creative applications. Is it right for you?

This course is suitable for learners who have some programming experience with Python or MATLAB, Octave, C/C++, Java, or Processing.

Upon the completion of this course, you’ll be equipped to train a computer to recognize objects in an image and use this knowledge to drive new and interesting behaviors.

You will also learn to train your models to understand the similarities and differences in large datasets and using them to self-organize, to understanding how to infinitely generate entirely new content or match the aesthetics or contents of another image.