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

## Creating the model
We can now train the Gradient Boosting Regressor using the `create_model()` function, which uses $k$-fold cross-validation to evaluate model accuracy. 

# Creating the model
We can now train the Gradient Boosting Regressor using the `create_model()` function, which uses $k$-fold cross-validation to evaluate model accuracy. 

In regression tasks, learn to create, tune, plot, save, and make predictions from the machine learning model.

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Building the Model

Creating the model