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

## Problem statement
In this challenge, we’ll plot `topic_distribution` on the Kiva dataset. First of all, we need to load the dataset to a variable named `data`. Second, we load the first 2000 dataset instances to the `data` variable. Third, we have to initialize the PyCaret NLP environment based on the aforementioned dataset, and set the target variable as `en`.

Furthermore, the initialized environment will be assigned to a variable named `nlp_setup`. After that, we need to create a **Latent Dirichlet Allocation** model and assign it to a variable named `lda_model`. Finally, we need to plot `topic_distribution`.
## Expected output
We should get an output plot as shown below:

# Problem statement
In this challenge, we’ll plot `topic_distribution` on the Kiva dataset. First of all, we need to load the dataset to a variable named `data`. Second, we load the first 2000 dataset instances to the `data` variable. Third, we have to initialize the PyCaret NLP environment based on the aforementioned dataset, and set the target variable as `en`.

Furthermore, the initialized environment will be assigned to a variable named `nlp_setup`. After that, we need to create a **Latent Dirichlet Allocation** model and assign it to a variable named `lda_model`. Finally, we need to plot `topic_distribution`.
# Expected output
We should get an output plot as shown below:

Create a Latent Dirichlet Allocation model and plot a topic distribution.

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Coding Challenge: Plot Topic Distribution

Problem statement

Expected output