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Introduction to Tensor Flow Machine Learning

Learn about the fundamentals of machine learning and the concept of TensorFlow in this free online course.

Publisher: NPTEL
This free online course on the Tensor Flow machine learning will introduce you to a brief history of TensorFlow. You will also be introduced to machine learning concepts and the mathematical foundation of deep learning. This course will be useful for Artificial Intelligence professionals who are focused on data processing and machine learning model building. By the end of this course, you will become familiar with machine learning concepts.
Introduction to Tensor Flow Machine Learning
  • Duration

    4-5 Hours
  • Students

    1,323
  • Accreditation

    CPD

Description

Modules

Outcome

Certification

View course modules

Description

This free online course the introduction toTensor Flow machine learning will begin by introducing you to the overview of TensorFlow and the concepts of machine learning. This course outlines the definition, brief history and gradual development of Tensorflow. You will also learn about the importance of Tensorflow, the contribution of TensorFlow to machine learning and the various methods of building a machine learning model.

This course explains in detail the basics of machine learning and machine learning concepts using neural network playground as a visualization tool. You will also learn about machine learning from a programming perspective and some important terminologies of machine learning. You will learn about the techniques for data normalization and the details of the machine learning process, including data pre-processing and model building. You will also learn about linear and logistic regression.

This course then analyzes the variations of gradient descent which are being employed in practical machine learning training. You will also learn about some of the nuances of gradient descent. This course explains the techniques surrounding model evaluation which include the basics of model evaluation and diagnosing problems with models. You will learn about the basic concepts, functions and attributes of tensors. You will also learn about matrix, the mathematical foundations of deep learning.

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