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

# Initializing the PyCaret environment
The `setup()` function initializes the PyCaret environment and creates a pipeline that prepares the data for model training and deployment. This is a powerful function with numerous data preprocessing features such as data imputation to deal with missing values, data transformation, data normalization, and many others. In this case, the default function parameters are acceptable, so we won’t make any changes. If you want to read more details about the `setup()` function and its numerous features, please refer to the [PyCaret Anomaly Detection](https://pycaret.readthedocs.io/en/latest/api/anomaly.html) module documentation page.

Get familiarized with the Pycaret environment setup for the anomaly detection task.

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Getting Familiar with the PyCaret Environment

Initializing the PyCaret environment