Anomalies

Name: Anomalies
Author: Briana Brownell

Explore the various methods of anomaly detection in machine learning.

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Course: On-Demand

In this chapter, we're gonna have a look at anomalies and the most common anomaly detection methods. The basic premise behind anomalies and anomaly detection is to look for outliers that don't fit with the rest of the data in some way. So the most obvious way to look at this is to look at the elements that are on the extreme sides of the dataset. So, the lowest items, as well as the highest items and this works pretty well if you have a relatively simple dataset where each dimension is completely independent of each other but in reality, most of the datasets that we're using have a number of factors in them and so the outliers may no longer be at the sort of edges of the dataset. So a lot of the anomaly detection algorithms that we're gonna use are going to be a little bit more sophisticated and dig into the data in a deeper way. Anomalies can be both intentional and unintentional. Intentional anomalies might be an indication of something that you, as a business, may wish to pay special attention to. Things like fraud often show up as anomalies, but in a lot of cases, the anomalies that we find are unintentional and so they may be evidence of data entry errors or data collection errors or data that hasn't quite been cleaned properly and so all of these things are relevant to anomaly detection and the reason that you want to pay attention to anomaly detection is that often when you're building a model, those anomalies can negatively affect your modeling by creating a model that's less accurate. Often when you're doing supervised machine learning, having anomalous data can have an outsize impact on the modeling results. So, it's something that you definitely want to pay attention to, regardless what kind of machine learning application that you have. Broadly, there are two styles of methods that you can use for anomaly detection, supervised and unsupervised. For supervised anomaly detection, you need to have a labeled dataset. So, you need to have a dataset that has a series of observations that are normal, as well as a series of observations that are fraudulent or otherwise anomalous. This allow allows you to use many of the different machine learning modeling techniques, such as classification modeling in order to detect the anomalies. The second method is using unsupervised machine learning methods and this is a little different because instead of having that labeled outcome variable, instead, you're looking at the dataset itself and you're trying to use the structure of the data in order to tell which cases are normal and which are outliers. Most often, in practice it's unsupervised methods that are used to detect anomalies and that's because it's usually really difficult to find labeled datasets that have labeled anomalies. So now you should have a little bit of an idea of what kinds of problems anomaly detection can solve and the basic two methods of an anomaly detection that are most often used in practice.

Intermediate Provider : Briana Brownell 5 Lessons · 16m in Arabic, German, English, Spanish, French, Portuguese, Urdu, Chinese

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.