Data Insights, Anomalies, & Verification: Handling Anomalies

Machine Learning
  • 10 Videos | 49m 3s
  • Includes Assessment
  • Earns a Badge
Likes 11 Likes 11
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

  • Playable
    1. 
    Course Overview
    1m 24s
    UP NEXT
  • Playable
    2. 
    Data and Anomaly Sources
    5m 25s
  • Locked
    3. 
    Decomposition and Forecasting
    4m 29s
  • Locked
    4. 
    Examine Data Using Randomization Tests
    4m 3s
  • Locked
    5. 
    Anomaly Detection
    4m 55s
  • Locked
    6. 
    Anomaly Detection Techniques
    5m 28s
  • Locked
    7. 
    Anomaly Detection with scikit-learn
    4m 44s
  • Locked
    8. 
    Anomaly Detection Tools
    6m 21s
  • Locked
    9. 
    Anomaly Detection Rules
    4m 23s
  • Locked
    10. 
    Exercise: Detecting Anomalies
    3m 50s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

Skillsoft is providing you the opportunity to earn a digital badge upon successful completion of this course, which can be shared on any social network or business platform

Digital badges are yours to keep, forever.

PEOPLE WHO VIEWED THIS ALSO VIEWED THESE