PyCaret is an open-source, machine learning library in Python. We can use it in machine learning tasks, from data preparation to model deployment.

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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

Clustering is considered to be a________ learning task.

Clustering is considered to be a________ learning task.

`create_model('kmeans', num_clusters = 4)`

Which function call will create a k-means clustering model with $4$ clusters.

Which function call will create a k-means clustering model with <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mn>4</mn></mrow><annotation encoding="application/x-tex">4</annotation></semantics></math>4 clusters.

A silhouette coefficient value close to ________ indicates that dataset instances have been assigned to the correct clusters.

A silhouette coefficient value close to ________ indicates that dataset instances have been assigned to the correct clusters.

The _________ plot can be used to estimate the optimal number of clusters.

The _________ plot can be used to estimate the optimal number of clusters.

Principal component analysis (PCA) is a _________ technique that can be used for visualization purposes.

Principal component analysis (PCA) is a _________ technique that can be used for visualization purposes.

This quiz will test your knowledge about clustering.

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Quiz: Clustering