Data Insights, Anomalies, & Verification: Handling Anomalies

Machine Learning    |    Intermediate
  • 10 videos | 45m 3s
  • Includes Assessment
  • Earns a Badge
Rating 4.4 of 9 users Rating 4.4 of 9 users (9)
In this 9-video course, learners examine statistical and machine learning implementation methods and how to manage anomalies and improvise data for better data insights and accuracy. The course opens with a thorough look at the sources of data anomaly and comparing differences between data verification and validation. You will then learn about approaches to facilitating data decomposition and forecasting, and steps and formulas used to achieve the desired outcome. Next, recall approaches to data examination and use randomization tests, null hypothesis, and Monte Carlo. Learners will examine anomaly detection scenarios and categories of anomaly detection techniques and how to recognize prominent anomaly detection techniques. Then learn how to facilitate contextual data and collective anomaly detection by using scikit-learn. After moving on to tools, you will explore the most prominent anomaly detection tools and their key components, and recognize the essential rules of anomaly detection. The concluding exercise shows how to implement anomaly detection with scikit-learn, R, and boxplot.

WHAT YOU WILL LEARN

  • List sources of data anomaly and compare the differences between data verification and validation
    Describe approaches of facilitating decomposition and forecasting, and list the steps and formulas used to achieve the desired outcome
    Recall data examination approaches, and use randomization tests, null hypothesis, and monte carlo
    Identify anomaly detection scenarios and categories of anomaly detection techniques
    Recognize prominent anomaly detection techniques
  • Demonstrate how to facilitate contextual data and collective anomaly detection using scikit-learn
    List prominent anomaly detection tools and their key components
    Recognize essential rules of anomaly detection
    Implement anomaly detection using scikit-learn, r, and boxplot

IN THIS COURSE

  • 1m 24s
  • 5m 25s
    After completing this video, you will be able to list sources of data anomalies and compare the differences between data verification and validation. FREE ACCESS
  • Locked
    3.  Decomposition and Forecasting
    4m 29s
    Upon completion of this video, you will be able to describe approaches of facilitating decomposition and forecasting, and list the steps and formulas used to achieve the desired outcome. FREE ACCESS
  • Locked
    4.  Examine Data Using Randomization Tests
    4m 3s
    After completing this video, you will be able to recall data examination approaches, and use randomization tests, the null hypothesis, and Monte Carlo. FREE ACCESS
  • Locked
    5.  Anomaly Detection
    4m 55s
    Learn how to identify anomaly detection scenarios and categories of anomaly detection techniques. FREE ACCESS
  • Locked
    6.  Anomaly Detection Techniques
    5m 28s
    After completing this video, you will be able to recognize prominent anomaly detection techniques. FREE ACCESS
  • Locked
    7.  Anomaly Detection with scikit-learn
    4m 44s
    In this video, you will learn how to facilitate contextual data and collective anomaly detection using scikit-learn. FREE ACCESS
  • Locked
    8.  Anomaly Detection Tools
    6m 21s
    After completing this video, you will be able to list prominent anomaly detection tools and their key components. FREE ACCESS
  • Locked
    9.  Anomaly Detection Rules
    4m 23s
    Upon completion of this video, you will be able to recognize essential rules for anomaly detection. FREE ACCESS
  • Locked
    10.  Exercise: Detecting Anomalies
    3m 50s
    Learn how to implement anomaly detection using scikit-learn, R, and the boxplot method. FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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