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

Machine Learning    |    Intermediate
  • 11 videos | 50m 25s
  • Includes Assessment
  • Earns a Badge
Rating 4.9 of 7 users Rating 4.9 of 7 users (7)
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

  • 1m 45s
  • 6m 17s
    Upon completion of this video, you will be able to describe the supervised and unsupervised approaches to anomaly detection. FREE ACCESS
  • Locked
    3.  Comparing Anomaly Detection Algorithms
    7m 18s
    In this video, find out how to compare the most prominent anomaly detection algorithms. FREE ACCESS
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    4.  Anomaly Detection Using R
    5m 14s
    In this video, you will learn how to detect anomalies using R, RCP, and the devtools package. FREE ACCESS
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    5.  Online Anomaly Detection Components
    6m 28s
    During this video, you will learn how to identify components of general online anomaly detection systems. FREE ACCESS
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    6.  Online Anomaly Detection Approaches
    3m 10s
    Upon completion of this video, you will be able to describe the approaches of using time series and windowing to detect anomalies. FREE ACCESS
  • Locked
    7.  Anomaly Detection Use Cases
    4m 57s
    Upon completion of this video, you will be able to recognize real-world use cases of anomaly detection as well as the steps and approaches adopted to handle the entire process. FREE ACCESS
  • Locked
    8.  Anomaly Detection with Visualization Tools
    4m 23s
    In this video, you will learn how to detect anomalies using a boxplot and a scatter plot. FREE ACCESS
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    9.  Anomaly Detection with Mathematical Approaches
    3m 49s
    In this video, learn how to detect anomalies using mathematical approaches. FREE ACCESS
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    10.  Cluster-Based Anomaly Detection
    4m 7s
    In this video, you will learn how to implement anomaly detection using a K-means machine learning algorithm. FREE ACCESS
  • Locked
    11.  Exercise: Detecting Anomalies
    2m 57s
    In this video, you will learn how to implement anomaly detection using visualization, clustering, and mathematical approaches. FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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