Data Insights, Anomalies, & Verification: Machine Learning & Visualization Tools
Machine Learning
| Intermediate
- 11 videos | 50m 25s
- 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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1m 45s
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6m 17sUpon completion of this video, you will be able to describe the supervised and unsupervised approaches to anomaly detection. FREE ACCESS
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3. Comparing Anomaly Detection Algorithms7m 18sIn this video, find out how to compare the most prominent anomaly detection algorithms. FREE ACCESS
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4. Anomaly Detection Using R5m 14sIn this video, you will learn how to detect anomalies using R, RCP, and the devtools package. FREE ACCESS
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5. Online Anomaly Detection Components6m 28sDuring this video, you will learn how to identify components of general online anomaly detection systems. FREE ACCESS
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6. Online Anomaly Detection Approaches3m 10sUpon completion of this video, you will be able to describe the approaches of using time series and windowing to detect anomalies. FREE ACCESS
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7. Anomaly Detection Use Cases4m 57sUpon completion of this video, you will be able to recognize real-world use cases of anomaly detection as well as the steps and approaches adopted to handle the entire process. FREE ACCESS
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8. Anomaly Detection with Visualization Tools4m 23sIn this video, you will learn how to detect anomalies using a boxplot and a scatter plot. FREE ACCESS
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9. Anomaly Detection with Mathematical Approaches3m 49sIn this video, learn how to detect anomalies using mathematical approaches. FREE ACCESS
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10. Cluster-Based Anomaly Detection4m 7sIn this video, you will learn how to implement anomaly detection using a K-means machine learning algorithm. FREE ACCESS
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11. Exercise: Detecting Anomalies2m 57sIn this video, you will learn how to implement anomaly detection using visualization, clustering, and mathematical approaches. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.
Digital badges are yours to keep, forever.
