Tensor Flow Machine Learning Transfer Learning | Free Course | Alison
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Tensor Flow Machine Learning Transfer Learning

Learn about the fundamentals of Convolutional Neural Network and transfer learning in this free online course.

Data Science
Free Course
This free online course in Tensor Flow Machine Learning transfer learning will introduce you to a new neural network architecture known as Convolutional Neural Network (CNNs). You will also learn about image classification and visualization as well as transfer Learning with pre-trained Convolutional Neural Network and TensorFlow hub. You will also be introduced to the method of using Estimator API to create machine learning models.
  • Duration

    4-5 Hours
  • Assessment

    Yes
  • Certification

    Yes
  • Responsive

    Yes
  • Publisher

    NPTEL

Description

Modules

Outcome

Certification

View course modules

Description

This free online course in Tensor Flow Machine Learning to transfer learning will begin by introducing you to the concept of Convolutional Neural Networks, otherwise known as CNNs. You will also learn about convolution operations, the procedure for the creation of filters as well as the evaluation of filters. This course will introduce you to the key operations in Convolution Neural Networks and demonstrate their usage in recognizing handwritten digit task. You will also learn about pooling, model summary and the differences between CNNs and feed-forward neural network.

This course then introduces the importance of pre-trained machine learning model as a feature extractor. You will also learn the procedure for fine-tuning a pre-trained model. Next, you will be introduced to the process of building image classification models from scratch using strategies employed in practice. You will also learn how to visualize what the Convolution Neural Network is learning.

The course then explains the process of using Tensorflow Keras as an API to build machine learning models. You will also learn about the overview of programming to estimators, input functions and feature columns. This course explains in great detail the importance of the boosted tree model and how to utilize Tensorflow estimator API to train boosted tree classifiers. You will learn how to create input functions and feature columns. You will also learn about the importance of embedding projectors, encoding, embedding layers and decoding

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Learning Outcomes

Upon successful completion of this course, you will be able to:


  • Explain the process of convolution operations
  • Discuss the key operations in CNNs and demonstrate their usage in recognizing handwritten digit task
  • Describe the process of using pre-trained machine learning model as a feature extractor
  • Explain the procedure of fine-tuning a pre-trained model
  • Discuss Estimator API and its application
  • Explain how to build a logistic regression classifier with Tensorflow estimator API
  • Define the boosted tree model and discuss its performance

Certification

All Alison courses are free to enrol, study and complete. To successfully complete this Certificate course and become an Alison Graduate, you need to achieve 80% or higher in each course assessment. Once you have completed this Certificate course, you have the option to acquire an official Certificate, which is a great way to share your achievement with the world. Your Alison Certificate is:

Ideal for sharing with potential employers - include it in your CV, professional social media profiles and job applications
An indication of your commitment to continuously learn, upskill and achieve high results
An incentive for you to continue empowering yourself through lifelong learning

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