Anomaly Detection Made Easy with PyCaret

Are you looking to add Anomaly Detection to your existing toolbox of skills?

Anomaly Detection is an essential application: it can identify potential problems and reduce false alarms.

From fraud detection to predictive maintenance, the possibilities are endless.

• Uncover hidden trends and patterns in the data with PyCaret's robust anomaly detection capabilities.

• Detect anomalies in customer behavior, product demand, etc.

• Discover new opportunities with PyCaret's anomaly detection by spotting abnormal patterns in sales and financial data.

Anomaly detection identifies outliers in any given situation.

Used for a wide range of use cases - to identify fraud in financial services and 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:

1. The types of Anomalies

2. Anomaly detection use cases

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

Anomaly detection is not just about finding outliers, but understanding the context of the data.

As you know all too well, without context, results are just numbers.

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

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

2. The range of anomaly detection algorithms

3. How to assign models

4. How to visualize the results of anomaly detection in PyCaret.

   
   

Combining Anomaly Detection with other Data Analytics techniques, such as clustering and regression, can provide a more comprehensive understanding of your data.

• Discover how PyCaret's cutting-edge anomaly detection algorithms can save your business thousands by detecting anomalies before they cause financial damage.

• Learn how to implement PyCaret's state-of-the-art anomaly detection models in your work, and see the results in real-time.

• Get the skills you need to identify complex anomalies in vast datasets with ease using PyCaret's user-friendly interface.

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

You can focus on mastering the simple PyCaret workflow and applying your intuition to draw relevant and useful conclusions informed by domain knowledge.

We first undertake exploratory data analysis using Python's Seaborn library. Then:

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

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

3. We discover that these content types have received many reactions, and the content types and reaction types vary from algorithm to algorithm. This is how we combine context and intuition with PyCaret’s powerful algorithm.

So, what are you waiting for?

• Discover the game-changing anomaly detection techniques using PyCaret that top data scientists use to stay ahead of their competition.

• Uncover how to quickly and accurately detect anomalies in your data, giving you a competitive edge in the workplace.

• Learn the skills to impress your boss and stand out in job interviews by demonstrating your proficiency in the hottest data analytics tool, PyCaret.

• Harness the power of PyCaret to detect hidden patterns and insights that your competition is missing and transform your data analysis skills into a valuable asset in the job market.

• Join the elite community of data professionals with a cutting-edge career advantage by mastering PyCaret for anomaly detection.

By the end of the course, you'll have hands-on experience and a solid understanding of the fundamentals of anomaly detection using PyCaret.

This course is ideal for data analysts, business analysts, citizen data scientists, students, and anyone interested in anomaly detection.