Course Description
These lessons will take you through various methods of anomaly detection and their real-world applications. You’ll learn how various styles of machine learning can be used in anomaly detection, and which specific clustering methods work for anomaly detection. In turn, we’ll explore isolation forests, and how they might apply to the real-world application of fraud detection.
In addition, this course will explain further how anomalies can be detected, as well as the differences between supervised and unsupervised methods of anomaly detection, k-nearest neighbors, and cluster-based methods of detecting anomalies in machine learning.
What You'll Learn
- Detect anomalies and identify the ways that data might be anomalous
- Distinguish between supervised, unsupervised, and semi-supervised methods of anomaly detection
- Apply clustering and cluster-based methods to detect anomalies in machine learning
- Use k-nearest neighbors for anomaly detection
- Explore isolation forests and apply them to fraud detection
- Examine the challenges faced in real-world applications of anomaly detection
Key Takeaways
- Various styles of machine learning can be used for anomaly detection, including supervised, unsupervised, and semi-supervised methods.
- Specific clustering and cluster-based methods can be applied to detect anomalies in data.
- Isolation forests are an anomaly detection method that can be applied to real-world fraud detection.
- K-nearest neighbors is one of the approaches covered for detecting anomalies in machine learning.
- Real-world applications of anomaly detection present challenges that the course explores.
Frequently Asked Questions
What does this course cover?
The course covers various methods of anomaly detection and their real-world applications, including how different styles of machine learning are used in anomaly detection, which clustering methods work for it, isolation forests, k-nearest neighbors, cluster-based methods, and the differences between supervised and unsupervised approaches.
What methods of anomaly detection will I learn?
You will learn about supervised vs. unsupervised methods, semi-supervised methods, k-nearest neighbors, cluster-based methods, and isolation forests.
What real-world application is used as an example?
The course explores isolation forests and how they might apply to the real-world application of fraud detection.
What skills does this course help build?
It helps build skills in anomaly detection, data mining methods, fraud prevention and detection, machine learning, machine learning algorithms, and machine learning methods.
What are the learning objectives?
To detect anomalies and ways that data might be anomalous, understand the challenges faced within real-world applications of anomaly detection, and review clustering methods for anomaly detection.
