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

Simplifying Machine Learning with PyCaret_ .png

c.tar.gz

jupyter

RunCode

spa1

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

Regression is a machine learning task where the goal is to predict a ________ value.

Regression is a machine learning task where the goal is to predict a ________ value.

The ________process lets us understand the statistical properties of a dataset and extract valuable insights from it.

The ________process lets us understand the statistical properties of a dataset and extract valuable insights from it.

Which PyCaret function can be used to load a dataset?

Which PyCaret function can be used to load a dataset?

The ________ metric is not provided in the `compare_models()` function results.

The ________ metric is not provided in the <code>compare_models()</code> function results.

Which PyCaret function is used to improve the performance of a trained machine learning model?

Which PyCaret function is used to improve the performance of a trained machine learning model?

This quiz will test your knowledge about regression.

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Quiz: Regression