Deep Learning for Anomaly Detection with Python

Deep Learning for Anomaly Detection with Python, available at $54.99, has an average rating of 4.2, with 42 lectures, based on 17 reviews, and has 199 subscribers.

You will learn about 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. This course is ideal for individuals who are Data scientists looking to expand their skill set in time series analysis and anomaly detection using Python. or Engineers interested in applying deep learning techniques to time series data for anomaly detection. or Data analysts who want to gain expertise in working with time series data and identifying anomalies. or Python programmers who want to explore real-world applications of Python in data analysis and anomaly detection. or Students and researchers studying data science, machine learning, or related fields who wish to enhance their practical skills. or IT professionals and analytics experts seeking to address anomaly detection challenges in their organizations. or Anyone with an interest in data analysis, time series data, and anomaly detection who wants to apply Python for practical solutions. It is particularly useful for Data scientists looking to expand their skill set in time series analysis and anomaly detection using Python. or Engineers interested in applying deep learning techniques to time series data for anomaly detection. or Data analysts who want to gain expertise in working with time series data and identifying anomalies. or Python programmers who want to explore real-world applications of Python in data analysis and anomaly detection. or Students and researchers studying data science, machine learning, or related fields who wish to enhance their practical skills. or IT professionals and analytics experts seeking to address anomaly detection challenges in their organizations. or Anyone with an interest in data analysis, time series data, and anomaly detection who wants to apply Python for practical solutions.

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Summary

Title: Deep Learning for Anomaly Detection with Python

Price: $54.99

Average Rating: 4.2

Number of Lectures: 42

Number of Published Lectures: 42

Number of Curriculum Items: 42

Number of Published Curriculum Objects: 42

Original Price: ₹799

Quality Status: approved

Status: Live

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.

Who Should Attend

• Data scientists looking to expand their skill set in time series analysis and anomaly detection using Python.
• Engineers interested in applying deep learning techniques to time series data for anomaly detection.
• Data analysts who want to gain expertise in working with time series data and identifying anomalies.
• Python programmers who want to explore real-world applications of Python in data analysis and anomaly detection.
• Students and researchers studying data science, machine learning, or related fields who wish to enhance their practical skills.
• IT professionals and analytics experts seeking to address anomaly detection challenges in their organizations.
• Anyone with an interest in data analysis, time series data, and anomaly detection who wants to apply Python for practical solutions.

Target Audiences

• Data scientists looking to expand their skill set in time series analysis and anomaly detection using Python.
• Engineers interested in applying deep learning techniques to time series data for anomaly detection.
• Data analysts who want to gain expertise in working with time series data and identifying anomalies.
• Python programmers who want to explore real-world applications of Python in data analysis and anomaly detection.
• Students and researchers studying data science, machine learning, or related fields who wish to enhance their practical skills.
• IT professionals and analytics experts seeking to address anomaly detection challenges in their organizations.
• Anyone with an interest in data analysis, time series data, and anomaly detection who wants to apply Python for practical solutions.