Data Insights, Anomalies, & Verification: Machine Learning & Visualization Tools

Machine Learning    |    Intermediate
  • 11 Videos | 54m 55s
  • Includes Assessment
  • Earns a Badge
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Discover how to use machine learning methods and visualization tools to manage anomalies and improvise data for better data insights and accuracy. This 10-video course begins with an overview of machine learning anomaly detection techniques, by focusing on the supervised and unsupervised approaches of anomaly detection. Then learners compare the prominent anomaly detection algorithms, learning how to detect anomalies by using R, RCP, and the devtools package. Take a look at the components of general online anomaly detection systems and then explore the approaches of using time series and windowing to detect online or real-time anomalies. Examine prominent real-world use cases of anomaly detection, along with learning the steps and approaches adopted to handle the entire process. Learn how to use boxplot and scatter plot for anomaly detection. Look at the mathematical approach to anomaly detection and implementing anomaly detection using a K-means machine learning approach. Conclude your coursework with an exercise on implementing anomaly detection with visualization, cluster, and mathematical approaches.

WHAT YOU WILL LEARN

  • describe the supervised and unsupervised approaches of anomaly detection
    compare the prominent anomaly detection algorithms
    demonstrate how to detect anomalies using R, RCP, and the devtools package
    identify components of general online anomaly detection systems
    describe the approaches of using time series and windowing to detect anomalies
  • recognize the real-world use cases of anomaly detection as well as the steps and approaches adopted to handle the entire process
    demonstrate detecting anomalies using boxplot and scatter plot
    demonstrate the mathematical approaches of detecting anomalies
    implement anomaly detection using a K-means machine learning approach
    implement anomaly detection with visualization, cluster, and mathematical approaches

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 45s
    UP NEXT
  • Playable
    2. 
    Machine Learning Anomaly Detection Techniques
    6m 17s
  • Locked
    3. 
    Comparing Anomaly Detection Algorithms
    7m 18s
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    4. 
    Anomaly Detection Using R
    5m 14s
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    5. 
    Online Anomaly Detection Components
    6m 28s
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    6. 
    Online Anomaly Detection Approaches
    3m 10s
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    7. 
    Anomaly Detection Use Cases
    4m 57s
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    8. 
    Anomaly Detection with Visualization Tools
    4m 23s
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    9. 
    Anomaly Detection with Mathematical Approaches
    3m 49s
  • Locked
    10. 
    Cluster-Based Anomaly Detection
    4m 7s
  • Locked
    11. 
    Exercise: Detecting Anomalies
    2m 57s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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