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

# Create K-Means model with 3 clusters and assign it to model variable.

 model = create_model('kmeans', num_clusters = 3)

# Assign cluster labels to the dataset and assign result to clusters variable.

 clusters = assign_model(model)

# Print the first 10 instances of the resulting dataset.

 clusters.head(10)

# Explanation

* **Line 3**: We create a $K$-Means model with 3 clusters using the `create_model()` function and assign it to the `model` variable.
* **Line 7**: We assign cluster labels to the dataset using the `assign_model()` function and assign the result to the `clusters` variable.

* **Line 11**: We print the first $10$ instances of the resulting dataset using the `head()` function.

This is the in-depth solution review of the "Create k-means Model" coding challenge.

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Solution Review: Create k-means Model

Solution

Explanation