Data Insights, Anomalies, & Verification: Handling Anomalies
Machine Learning
| Intermediate
- 10 videos | 45m 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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1m 24s
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5m 25sAfter completing this video, you will be able to list sources of data anomalies and compare the differences between data verification and validation. FREE ACCESS
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3. Decomposition and Forecasting4m 29sUpon 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
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4. Examine Data Using Randomization Tests4m 3sAfter completing this video, you will be able to recall data examination approaches, and use randomization tests, the null hypothesis, and Monte Carlo. FREE ACCESS
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5. Anomaly Detection4m 55sLearn how to identify anomaly detection scenarios and categories of anomaly detection techniques. FREE ACCESS
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6. Anomaly Detection Techniques5m 28sAfter completing this video, you will be able to recognize prominent anomaly detection techniques. FREE ACCESS
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7. Anomaly Detection with scikit-learn4m 44sIn this video, you will learn how to facilitate contextual data and collective anomaly detection using scikit-learn. FREE ACCESS
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8. Anomaly Detection Tools6m 21sAfter completing this video, you will be able to list prominent anomaly detection tools and their key components. FREE ACCESS
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9. Anomaly Detection Rules4m 23sUpon completion of this video, you will be able to recognize essential rules for anomaly detection. FREE ACCESS
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10. Exercise: Detecting Anomalies3m 50sLearn 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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Digital badges are yours to keep, forever.
