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Diploma in Practical Machine Learning with Tensor Flow

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

Publisher: NPTEL
This free online course on practical machine learning and TensorFlow will be particularly useful for technology companies, computer engineers. It will also be useful for Artificial Intelligence professionals who deal with data processing, as well as machine learning model building. By the end of this course, you will become more familiar with the concepts of TensorFlow, machine learning models and neural networks.
Diploma in Practical Machine Learning with Tensor Flow
  • Duration

    9-10 Hours
  • Students

    2,767
  • Accreditation

    CPD

Description

Modules

Outcome

Certification

View course modules

Description

This free online course in practical machine learning with TensorFlow will begin by introducing you to the concept of machine learning and the overview of TensorFlow. You will learn about the steps in the machine learning process, logistic regression and the loss unction in machine learning. You will also be introduced to gradient descent, gradient descent variations, machine learning visualization as well as confusion matrix.

The course then introduces the concept of tensors and their relevance. You will learn about the mathematical fundamentals of deep learning. You will also learn how to build data pipelines for TensorFlow as well as text processing with TensorFlow. Next, you will be introduced to machine learning models, text classification, overfitting, underfitting, regression and the architecture of neural network model.

The course then explains the meaning of convolution neural network and transfer learning. You will also learn about the pooling, and image classification and visualization. This course explains in great detail estimator API, boosted trees as well as word embeddings and its application. This course also explains the customization of TensorFlow, writing a custom layer as well as Tensorflow distributed training.

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