Anomaly Detection with PyCaret
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
- Basic Python and an understanding of the intuition behind various Machine Learning Algorithms
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 vital for every data scientist.
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
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
With a goal to enhance the level of data literacy in 1 million businesses, to help analysts and managers like you break through decision-making silos in your organization so you can make the most impactful change together with the decision-makers in your institutions using data.
Built by professionals who provide decision-making support leveraging the power of data to businesses, governments and large international organizations including the United Nations, DatOlympia Learning Solutions is excited for the possibility of low code/no code solutions to evangelize data literacy in 1 million businesses all over the world.