TensorFlow is a state-of-the-art, open source machine learning framework created by Google to design, build, and train Machine Learning and Deep learning models.

TensorFlow has a comprehensive and flexible ecosystem of tools and community resources that make it easy to develop and train ML and Deep Learning models.

I know the options out there; prerequisites and the skills you need to acquire to overcome the learning blocks. So, Please refer to the Closing Notes section at the tail end of this piece, where you will find helpful resources for bootstrapping your intellectual abilities.

My goal in this piece is to help you find some interactive courses from the Notable Educators that will edify you with the solid understanding of TensorFlow.

So, without further ado, let’s get started.

— Introduction to TensorFlow in Python

This course is designed by Isaiah Hull, a senior economist working at Swiss Central Bank and delivered via DataCamp.

This course covers all the concepts you need to know in fundamentals of neural networks, making use of high-level API’s and building deep learning models using TensorFlow. Is it right for you? This course is suitable for learners with good experience in Python and high school-level maths. GO TO DATACAMP

— Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

This course is taught by Laurence Moroney and is part of the TensorFlow in Practice Specialization ( sectioned below ) by Deeplearning.ai.

Through the guided series of lectures and exercises to learn TensorFlow, you’ll build and train neural network for computer vision application and finally learn to use convolutions to improve your models. Is it right for you? This course requires intermediate knowledge in Python and high school-level maths. By the end, you will be highly prepared to learn advanced topics in Computer vision, TensorFlow and Machine Learning. GO TO COURSERA

— Introduction to TensorFlow in R

This interactive course in R programming is created by Colleen Bobbie, and delivered via DataCamp.

This course starts from simple linear regressions to more complex deep learning neural networks, and you will also learn the basics of TensorFlow and higher-level APIs such as Keras and TFEstimators. Is it right for you? This course is suitable for learners with intermediate knowledge of R programming and high school-level maths. GO TO DATACAMP

— Introduction to TensorFlow

This course is designed by Google Cloud Engineers and delivered via Coursera.

This course introduces you to the low-level of TensorFlow to help you understand the necessary concepts and APIs that equip you with the skills to write distributed machine learning models. Is it right for you? This is an intermediate level course, suitable for learners with knowledge in programming, cloud computing and high school-level maths. GO TO COURSERA

— Convolutional Neural Networks in TensorFlow

This high-rated course designed by Deeplearning.ai is part of TensorFlow in practice specialisation ( sectioned below ) and delivered via Coursera.

This course aims to handle ConvNets with real-world image data to help you learn the techniques of improving your ConvNet performance when doing image classification. You will also learn about plot loss and accuracy, and understand how to prevent overfitting, including augmentation and dropout. Is it right for you? This specialization is suitable for experienced programmers with a solid background in maths. By the end, you will have gained skills in Inductive Transfer, Augmentation Dropouts, and Machine Learning . GO TO COURSERA

— TensorFlow in Practice Specialization

This four-course specialization is designed by Deeplearning.ai and delivered via Coursera.

In this specialization, you’ll learn the best practices to build and train a neural network for a computer vision applications with TensorFlow. You will also learn to apply RNNs, GRUs, and LSTMs as you train your models using text repositories. Is it right for you? This specialization is suitable for intermediate learners with experience in programming and knowledge of high school-level maths. GO TO COURSERA

— TensorFlow: Data and Deployment Specialization

This four-course specialization by Deeplearning.ai, complements TensorFlow in Practice specialization ( sectioned above ) with much advanced content.

You will learn the deployment scenarios and understand efficient ways to use data when training your models and also run them in your browser using TensorFlow.js By the end, you will gain life-long skills in Machine Learning, Advanced deployment, Object Detection and JavaScript. Is it right for you? This specialization is suitable for learners with intermediate programming skills and solid knowledge in maths. GO TO COURSERA

— Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization

This high-rated ( 5-course ) specialization is designed by Google Cloud Engineers and delivered via Coursera.

In this specialization, you learn advanced machine learning topics using Google Cloud Platform with hands-on lab experience in optimizing, deploying, and scaling production ML models. You will learn to build scalable, accurate, and production-ready models for structured data, image data, time-series, and natural language text. Is it right for you? This course is suitable for advanced learners with rich experience and knowledge about cloud computing, programming, and maths. By the end, you will be highly prepared to build recommendation systems. GO TO COURSERA

Closing Notes

You need to have a fairly good understanding of advanced mathematical concepts to become successful in the field of AI, Machine learning or Deep learning.

The following courses will be of tremendous help to you.

Thanks for making it to the end ;)

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