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.

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.