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Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4

This course will teach you how to create deep-learning algorithms for detecting and mitigating anomalies in data such as time series.

Andrei Pruteanu - Pluralsight course - Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4
by Andrei Pruteanu
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What you'll learn

In this course, Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4, you’ll learn to spot specific patterns in large datasets that can be labelled as anomalies. First, you’ll explore how to precisely define anomalies in data. Next, you’ll discover detection algorithms. Finally, you’ll learn how to mitigate anomalous data. When you’re finished with this course, you’ll have the skills and knowledge of creating machine learning algorithms needed for dealing with various anomalies in data.

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About the author

Andrei Pruteanu - Pluralsight course - Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4
Andrei Pruteanu

Andrei is a passionate Data Scientist. He started his career in tech in the automotive industry. After that he pursued a PhD in CS at Delft University of Technology, the Netherlands. Since the graduation of his studies, he worked with large data-sets in domains ranging from scientific research, energy and utilities. Currently he is consultant in Data Science and working mainly with NLP tools. He enjoys being part of the analytics community and regularly joins conferences and specific meetups

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