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 the following challenge, we’ll evaluate and plot a classification model by making predictions on the test dataset that is created during PyCaret environment initialization. The model variable is `classification_model`, and the result will be assigned to a variable named `predictions`. After doing that, we are going to finalize the model and assign it to the `final_model` variable. Finally, we will plot the decision boundaries of the model, by using the appropriate function and parameter.
# Expected output

Evaluate a classification model by making predictions on the test dataset.

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Coding Challenge: Make Predictions and Plot Model

Problem statement