Anomaly Detection with PyCaret

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[b]Anomaly Detection with PyCaret[/b]
Instructors: DatOlympia Learning Solutions
4 sections • 17 lectures • 1h 50m total length
Video: MP4 1280x720 44 KHz | English + Sub
Updated 12/2021 | Size: 696 MB

Unsupervised learning: Anomaly Detection with PyCaret Workflow

What you'll learn
Acquire an understanding of the intuition and some core concepts underlying Anomaly detection
Propose and formulate anomaly detection problem statements which can be effectively addressed in PyCaret
Grasp how PyCaret eases the workflow (including preprocessing) through a handful of easy steps
Manage a simple PyCaret workflow for anomaly detection
Requirements
Basic Python and an understanding of the intuition behind various Machine Learning Algorithms
Description
Anomaly detection identifies outliers in any given situation. Used for a wide range of use cases - to identify fraud in financial services, and for predictive maintenance in manufacturing, for identifying fake news in social media management, understanding the intuition behind anomaly detection is a critical tool in every data scientist's toolbox.

The course begins with an introduction to Anomaly Detection

The types of Anomalies

Anomaly detection use cases

Intuition behind some of the anomaly detection algorithms: Isolation Forest, Local Outlier Factor and KNN

In the second part of the course, we go through a discussion on the PyCaret workflow

How the PyCaret library simplifies data-cleaning and preparation for anomaly detection

The range of anomaly detection algorithms available

How to assign models

How to visualize the results of anomaly detection in PyCaret.

In the third and final part of the course, we work with an inbuilt PyCaret social media dataset (the 'Facebook' dataset)

We first undertake exploratory data analysis using Python Seaborn

We identify anomalies based on the reactions to posts/videos/links and other content types etc. In this case, the problem statement is to identify content which might need to be reviewed owing to the disproportionate number of reactions.

We work with a handful of anomaly detection models, and examine the dataset for the observations which are flagged as anomalous.

We discover that these are content types which have received a large number of reactions, and the content types and reaction types vary from algorithm to algorithm.

Who this course is for
Certified fraud examiners looking to apply AutoML tools
Analysts looking to deply AutoML tools for anomaly detection in sectors such as banking, healthcase, predictive maintenance in manufacturing operations
Low-code Machine Learning enthusiasts looking to learn anomaly detection
Beginner data scientists curious about AutoML tools and anomaly detection

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