Data Insights, Anomalies, & Verification: Machine Learning & Visualization Tools
Machine Learning
11 Videos | 54m 55s - Includes Assessment
- Earns a Badge
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
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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
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1.Course Overview1m 45sUP NEXT
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2.Machine Learning Anomaly Detection Techniques6m 17s
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3.Comparing Anomaly Detection Algorithms7m 18s
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4.Anomaly Detection Using R5m 14s
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5.Online Anomaly Detection Components6m 28s
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6.Online Anomaly Detection Approaches3m 10s
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7.Anomaly Detection Use Cases4m 57s
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8.Anomaly Detection with Visualization Tools4m 23s
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9.Anomaly Detection with Mathematical Approaches3m 49s
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10.Cluster-Based Anomaly Detection4m 7s
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11.Exercise: Detecting Anomalies2m 57s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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