Course Overview of AI-Driven Anomaly Detection:

Whether your organization needs to detect cybersecurity threats, fraudulent financial transactions, product defects, or equipment health issues, artificial intelligence offers powerful tools to catch anomalies before they become critical. AI models can be trained to automatically analyze datasets, learn “normal behavior,” and detect breaches in expected patterns with high accuracy. These models can even predict future anomalies by identifying subtle deviations that may be hard for humans to see.

With massive amounts of data and increasingly complex signals differentiating normal and abnormal behavior, organizations rely on advanced AI-based anomaly detection to safeguard operations, reduce risk, and optimize performance. This course equips learners with hands-on experience in preparing data, building models, and applying state-of-the-art techniques for robust anomaly detection.

After completing AI-Driven Anomaly Detection, participants will be able to:

  • Prepare data and build, train, and evaluate models using XGBoost, autoencoders, and GANs.
  • Detect anomalies in datasets using both labeled and unlabeled data.
  • Classify anomalies into multiple categories, even when the original data is not labeled.

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Key Features of AI-Driven Anomaly Detection:

  • Hands-on training using XGBoost, autoencoders, and GAN-based models.

  • Techniques for supervised, unsupervised, and semi-supervised anomaly detection.

  • Use of NVIDIA RAPIDS™, TensorFlow, Keras, pandas, and GPU-accelerated workflows.

  • Practical projects using real network anomaly datasets.

  • NVIDIA DLI Certificate of Competency upon successful assessment completion.

  • Dedicated cloud-based GPU server access during the workshop.

Who should Attend?

  • Data scientists working in cybersecurity, finance, IoT, and operations.
  • Machine learning practitioners implementing anomaly detection systems.
  • Professionals with experience in deep learning seeking specialized AI skills.
  • Teams aiming to deploy automated anomaly detection in business applications.

Prerequisites of AI-Driven Anomaly Detection:

  • Professional data science experience using Python.
  • Experience training deep neural networks.
  • Suggested materials to satisfy prerequisites: Getting Started with Deep Learning, Intro to Machine Learning.

    • Why choose CloudThat as your training partner?

    • Specialized instructor-led training focused on real-world anomaly detection workflows.
    • Certified trainers with hands-on experience using XGBoost, autoencoders, GANs, and NVIDIA RAPIDS™.
    • Fully practical labs covering supervised, unsupervised, and GAN-based detection methods.
    • Customized learning paths based on experience level and organizational goals.
    • Interactive sessions with live Q&A, step-by-step guidance, and mentoring.
    • Support for interview preparation, resume enhancement, and career transitions in AI roles.
    • Regular curriculum updates to include the latest tools, GPU technologies, and industry applications.
    • Positive learner reviews highlighting practical relevance and ease of understanding.

    Course Outline of AI-Driven Anomaly Detection Download Course Outline

    • Meet the instructor.
    • Create an account at courses.nvidia.com/join.

    • Prepare data for GPU acceleration using the provided dataset.
    • Train a binary and multi-class classifier using XGBoost.
    • Assess and improve your model’s performance before deployment.

    • Build and train a deep learning autoencoder for unlabeled data.
    • Apply techniques to separate anomalies into multiple classes.
    • Explore additional applications of GPU-accelerated autoencoders.

    • Train a GAN-based unsupervised learning model to generate new data.
    • Use generated data to transform the anomaly detection task into a supervised problem.
    • Compare this approach with traditional methods to evaluate improvements.

    • Final assessment of competencies.
    • Open Q&A session (15 minutes).

    Certification Details:

      Upon successful completion of the assessment, participants will receive an NVIDIA Deep Learning Institute certificate recognizing their subject matter expertise and supporting professional career growth.

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    Course ID: 27014

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    FAQs:

    The course duration is 8 hours.

    The course uses NVIDIA RAPIDS™, XGBoost, TensorFlow, Keras, pandas, autoencoders, and GANs.

    Yes. Participants receive an NVIDIA DLI certificate upon passing the assessment.

    The price is $500 for public workshops; enterprise workshop pricing is available on request.

    A desktop or laptop capable of running the latest version of Chrome or Firefox. GPU-accelerated cloud servers are provided.

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