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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

The task of classifying dataset instances in two groups is known as _________ classification.

The task of classifying dataset instances in two groups is known as _________ classification.

A classification model will have ________ performance on a dataset with balanced classes.

A classification model will have ________ performance on a dataset with balanced classes.

Which function call will train a logistic regression model with PyCaret?


Which function call will train a logistic regression model with PyCaret?

Which cross-validation method is used to evaluate classification models in PyCaret by default?


Which cross-validation method is used to evaluate classification models in PyCaret by default?

Which plot can be used to evaluate classification model accuracy?

Which plot can be used to evaluate classification model accuracy?

This quiz will test your knowledge about classification.

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Quiz: Classification