Data Insights, Anomalies, & Verification: Handling Anomalies
Machine Learning
10 Videos | 49m 3s - Includes Assessment
- Earns a Badge
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
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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
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1.Course Overview1m 24sUP NEXT
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2.Data and Anomaly Sources5m 25s
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3.Decomposition and Forecasting4m 29s
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4.Examine Data Using Randomization Tests4m 3s
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5.Anomaly Detection4m 55s
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6.Anomaly Detection Techniques5m 28s
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7.Anomaly Detection with scikit-learn4m 44s
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8.Anomaly Detection Tools6m 21s
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9.Anomaly Detection Rules4m 23s
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10.Exercise: Detecting Anomalies3m 50s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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