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Hands-On Unsupervised Learning with TensorFlow 2.0

As Machine Learning becomes increasingly important to businesses, AI helps unsupervised learning in categorizing data. Learn to use unsupervised learning algorithms with TensorFlow to make a model.

Features Includes:
  • Self-paced with Life Time Access
  • Certificate on Completion
  • Access on Android and iOS App

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Description

Learn unsupervised learning in Python with hands-on practical applications for each learning model.

Nowadays, machine learning is becoming increasingly important to businesses. It is used to solve various business problems using supervised and unsupervised algorithms. In unsupervised learning, Artificial Intelligence systems try to categorize unlabeled and unsorted data based on the similarities and differences that exist among data. In this case, the capabilities of unsupervised learning methods to generate a model based on data make it possible to deal with complex and more difficult problems in comparison with the capabilities of supervised learning. In this course, we examine different unsupervised learning methods and solve practical problems using the TensorFlow platform. Solving examples of real-world problems using TensorFlow is more inspiring and compelling and will enhance your practical skills.

By the end of this course, you will gain significant hands-on experience using unsupervised learning algorithms with TensorFlow and will be able to make your own model to solve relevant real-world learning problems.

All the code and supporting files for this course are available on GitHub at https://github.com/PacktPublishing/Hands-on-Unsupervised-Learning-with-TensorFlow-2.0

About the Author

  • Mahsa Lotfi has more than 4 years' experience in digital signal processing and data science. She has implemented academic projects in different fields including machine learning, big data, data compression, deep learning, and biomedical image processing and has programming experience in Python, C++, Matlab, Hadoop, and more. She achieved her Ph.D., Master’s, and Bachelor’s degrees in Electrical Engineering (the Digital Signal Processing branch) and is interested in problem-solving using math and data science algorithms.

Basic knowledge
  • Basic knowledge of programming with Python

What will you learn
  • The fundamentals of unsupervised learning algorithms and their importance
  • TensorFlow 2.0 terminology
  • Hands-on experience solving real-world problems in unsupervised learning
  • A practical approach to solving business problems, ranging from data preprocessing to model-building from a given dataset
Course Curriculum
Number of Lectures: 27 Total Duration: 02:30:22
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