This course, “Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4,” will expose you to the technique of showcasing and handling patterns in data, specifically time series data. Let me begin by defining anomalies and why they are important to examine and study. After that, different algorithms tailored to detecting these anomalies will be examined while employing the strength of the TensorFlow 2. 4 framework.
You will also learn how to manage and reduce the presence of anomalous data to keep your datasets clean and as efficient as possible. Upon completing the course “Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4,” the candidate shall be fully capable of applying and designing complex machine learning models of higher level for detecting and dealing with numerous types of data irregularities, which would enable the candidate to address data cases efficiently in the real world.
Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 Table of Contents:
- Course Overview: 2 minutes
- Introduction: 16 minutes
- Exploratory Data Analysis: 12 minutes
- Definition and Anomaly Types: 13 minutes
- Detection Algorithms: 39 minutes
- Mitigation Techniques: 8 minutes
Who is this course for?
- Data Scientists and Analysts: Its targeted audience is professional workers who want to enhance their knowledge about anomaly detection and prevention on large data sets.
- Machine Learning Engineers: The target audience is learners interested in enhancing their knowledge of implementing and using deep learning algorithms through TensorFlow 2. 4.
- Researchers and Academics: Those involved in research that analyses data based on the accuracy of facts, especially in time series data.
- Advanced Students: The relevant audience would be learners with machine learning and data analysis backgrounds to further detail anomaly detection strategies.
