Udemy – Deep Learning for Anomaly Detection with Python 2024-3

Deep Learning for Anomaly Detection with Python. This course teaches you how to identify anomalies in time series data using Python and deep learning. In this comprehensive course, you will dive deep into the world of time series data and equip yourself with the skills you need to effectively identify and analyze anomalies. Whether you are a data enthusiast, a budding data scientist, or an expert looking to enhance your data analysis skills, this course is your gateway to becoming a skilled anomaly detection expert.

What you will learn:

• Understand the principles 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 skills 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.
• Investigating hyperparameter tuning to optimize model performance and efficiency.
• Developing the ability to train and evaluate a time series anomaly detection model using Python.
• Understand how to set thresholds to detect anomalies based on mean absolute error (MAE).
• Gain practical experience in visualizing anomalies in time series data using Python’s matplotlib library.
• Learn how to interpret and use anomaly detection model results for real-world applications.
• Acquire essential Python skills for working with time series data and machine learning.
• Apply the knowledge gained in the course to analyze and detect anomalies in diverse time series data sets and real-world scenarios.

Who is this course suitable for?

• 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 looking to solve anomaly detection challenges in their organizations.
• Anyone interested in data analysis, time series data, and anomaly detection and wants to apply Python to practical solutions.

Course details: Deep Learning for Anomaly Detection with Python

• Publisher:  Udemy
• Lecturer:  Karthik Karunakaran, Ph.D.
• Training level: Beginner to advanced
• Training duration: 1 hour and 25 minutes
• Number of lessons: 42

Course headings

Prerequisites for the Deep Learning for Anomaly Detection with Python course

• Basic programming knowledge is recommended, but not mandatory. Familiarity with Python programming will be helpful.
• A Google account is required to access Google Drive and Google Colab for practical exercises.
• Access to a computer with a stable internet connection is necessary to access online resources and run code in the Google Colab environment.

Course images

Sample course video

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171 MB