TensorFlow Certification is a testament to developers’ expertise in machine learning. The certification programme essentially consists of an assessment examination developed by the TensorFlow team. On passing the exam, developers can join the TensorFlow Certificate Network.

Becoming an expert in TensorFlow is no easy feat. The candidates will learn to build models via Computer Vision, Convolutional Neural Networks(CNNs), and Natural Language Processing(NLP). Certification is ideal for developers who wish to build applications that use Machine Learning techniques.

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We have curated a list of courses and study sources to help you learn the concepts of TensorFlow.

Introduction to Deep Learning: MIT

MIT’s course, as the name suggests, is an introductory course taught by PhD researchers. The course consists of videos and review papers, same as those delivered to MIT students, and takes around 24-hours for a learner to complete.

The prerequisite for taking the course includes knowledge of calculus(differentiation and integration), linear algebra(matrices), and probability (Baye’s theorem). Working knowledge of Python is desirable but not compulsory.

The course focuses on applying deep learning to Natural language processing, computer vision, and more. The course teaches the basics of deep learning algorithms and provides practical experience and training to develop neural networks in TensorFlow.

For more information, click here.

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Free Resources To Prepare For TensorFlow Developer Certification
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