How do you find the most interesting or suspicious points within your data? What libraries and techniques can you use to detect these anomalies with Python? This week on the show, we speak with author Brett Kennedy about his book “Outlier Detection in Python.”
Brett describes initially getting involved with detecting outliers in financial data. He discusses various applications and techniques in security, manufacturing, quality assurance, and fraud. We also dig into the concept of explainable AI and the differences between supervised and unsupervised learning.
This episode is sponsored by APILayer.
Course Spotlight: Using k-Nearest Neighbors (kNN) in Python
In this video course, you’ll learn all about the k-nearest neighbors (kNN) algorithm in Python, including how to implement kNN from scratch. Once you understand how kNN works, you’ll use scikit-learn to facilitate your coding process.
Topics:
- 00:00:00 – Introduction
- 00:01:56 – Describing the book
- 00:03:22 – How did you get involved in outlier detection?
- 00:06:50 – Initially looking at the data to spot errors
- 00:08:22 – Amount of fraud and financial errors
- 00:09:50 – Understanding the nature of the outliers
- 00:12:15 – Industries that would be interested in detection
- 00:18:21 – Sponsor: APILayer.com
- 00:19:15 – Who is the intended audience for the book?
- 00:22:16 – Differences between supervised vs unsupervised learning
- 00:25:48 – Autonomous vehicles detecting anomalous imagery
- 00:29:08 – What is explainable AI?
- 00:36:21 – Video Course Spotlight
- 00:37:43 – Detecting an outlier across multiple columns
- 00:44:32 – Detection of LLM and bot activity
- 00:49:49 – Proving you are a human checkbox
- 00:52:25 – What are Python libraries for outlier detection?
- 00:53:57 – Creating synthetic data to work through examples
- 00:57:10 – Tools developed and described in the book
- 01:01:29 – How to find the book
- 01:02:27 – What are you excited about in the world of Python?
- 01:04:55 – What do you want to learn next?
- 01:05:52 – How can people follow your work online?
- 01:06:16 – Thanks and goodbye
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