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Hands-On Unsupervised Learning With TensorFlow 2.0

Hands-On Unsupervised Learning With TensorFlow 2.0

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.

Overview

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.

Course Information

Basic knowledge of programming with Python

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

This course targets aspiring data scientists, data analysts, machine learning engineers, and more.
This course will facilitate your understanding of the intricacies of each model and how to code it.
This course will help project managers, business analysts, and team leaders learn which unsupervised learning model to use for a specific business problem. It will also help people who are trying to build a career in Artificial Intelligence, data science, and machine learning understand models and their applications.

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Course Specifications

IT and Computing courses are available to study on our learning platform. 

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Adult education is the non-credential activity of gaining skills and improved education. 

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Online education is electronically supported learning that relies on the Internet for teacher/student interaction. 

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A short course is a learning programme that gives you combined content or specific skills training in a short period of time. Short courses often lean towards the more practical side of things and have less theory than a university course – this gives you a more hands-on experience within your field of interest.

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Course duration is 24 hours.

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