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

What type of task is anomaly detection?

A standard method of visualizing variable outliers is the ________ plot.

A standard method of visualizing variable outliers is the ________ plot.

The anomaly scores that are assigned to outliers by the local outlier factor model are ________.

The anomaly scores that are assigned to outliers by the local outlier factor model are ________.

The anomaly label that is assigned to dataset inliers by PyCaret is ________.

The anomaly label that is assigned to dataset inliers by PyCaret is ________.

The default proportion of outliers in the `create_model()` function is ___.

The default proportion of outliers in the <code>create_model()</code> function is ___.

This quiz will test your knowledge about anomaly detection.

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Quiz: Anomaly Detection