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

# Creating and assigning the model
We use the `create_model()` function to train the local outlier factor model on the Wholesale Customers dataset. After that, we assign anomaly labels and scores to the dataset using the `assign_model()` function.

In anomaly detection tasks, learn to create, evaluate, plot, save the machine learning model and get information about inliers and outliers. 

Building the Model

In anomaly detection tasks, learn to create, evaluate, plot, save the machine learning model and get information about inliers and outliers.

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Building the Model

Creating and assigning the model