Deep Learning for Anomaly Detection with Python
Time Series Anomaly Detection: Deep Learning Techniques for Identifying and Analyzing Anomalies in Time Series Data
3.88 (25 reviews)
241
students
1.5 hours
content
Mar 2024
last update
$59.99
regular price
What you will learn
Understand the fundamentals of time series data and its applications in anomaly detection.
Learn how to access and load time series data, specifically from the Numenta Anomaly Benchmark dataset, using Python.
Gain proficiency in data preprocessing techniques to ensure data is ready for analysis and modeling.
Master the creation of training and testing sequences for time series data in Python.
Build a deep learning model using Python's TensorFlow and Keras libraries, specifically an autoencoder, for time series anomaly detection.
Explore hyperparameter tuning to optimize model performance and efficiency.
Develop the ability to train and evaluate a time series anomaly detection model using Python.
Understand how to set a threshold for detecting anomalies based on Mean Absolute Error (MAE) loss.
Gain practical experience in visualizing anomalies within time series data using Python's matplotlib library.
Learn how to interpret and use the results of the anomaly detection model for real-world applications.
Acquire essential Python skills for working with time series data and machine learning.
Apply knowledge gained in the course to analyze and detect anomalies in diverse time series datasets and real-world scenarios.
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udemy ID
9/16/2023
course created date
2/29/2024
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