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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 the topic model
The `create_model()` function can be used to easily train a topic model on the preprocessed dataset. In this case, we train a LDA model with five topics, one for each of the dataset classes. After doing that, we use the `assign_model()` function that assigns the topic proportions to the dataset, as well as the dominant topic. Finally, we create a new dataset containing the topic proportions as well as the news article class, and print the first 10 instances.


# Creating the topic model
The `create_model()` function can be used to easily train a topic model on the preprocessed dataset. In this case, we train a LDA model with five topics, one for each of the dataset classes. After doing that, we use the `assign_model()` function that assigns the topic proportions to the dataset, as well as the dominant topic. Finally, we create a new dataset containing the topic proportions as well as the news article class, and print the first 10 instances.


Learn to create, assign, and plot the topic model in natural language processing tasks.

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Creating and Assigning the Topic Model

Creating the topic model