Gain insights into simplifying machine learning with PyCaret. Delve into regression, classification, clustering, and anomaly detection, and learn to deploy applications using Streamlit.

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PyCaret is an low-code, open-source, machine learning library for Python. It can be used in machine learning tasks such as data preparation and model deployment.

In this course, you will learn multiple topics related to machine learning. You will start with a brief introduction to the basic concepts of machine learning, and then continue with case studies of regression, classification, clustering, and anomaly detection based on the respective modules of the PyCaret library. Finally, we will focus on using the Streamlit library to develop and deploy machine learning applications.

By the end of this course, you will have the skills to deploy the robust PyCaret library for any of your machine learning projects.

Simplifying Machine Learning with PyCaret in Python

## Problem statement
In this challenge, we’ll create and label a local outlier factor model with an outlier proportion of $10$% and assign it to the `model` variable. Afterward, we must assign anomaly labels and scores to the `data_assigned` variable. Now, we need to create a new dataset named `data_outliers`, containing only the instances that have been labeled as outliers. Finally, print the first $5$ instances of the outlier dataset.
## Expected output

# Problem statement
In this challenge, we’ll create and label a local outlier factor model with an outlier proportion of $10$% and assign it to the `model` variable. Afterward, we must assign anomaly labels and scores to the `data_assigned` variable. Now, we need to create a new dataset named `data_outliers`, containing only the instances that have been labeled as outliers. Finally, print the first $5$ instances of the outlier dataset.
# Expected output

Create a local outlier factor model with an outlier proportion of 10%, assign anomaly labels and print the outlier dataset.

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Coding Challenge: Create Local Outlier Factor Model

Problem statement

Expected output