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

# Classification environment setup
Now that we have a dataset with 5 numeric features and a class label, we can train a classification model on it. By doing that, we’ll be able to create a text classification pipeline using the topic and classification models. At this point, we’re dealing with a multiclass classification task. We’ll discuss that more briefly.

First of all, we initialize the PyCaret classification environment that prepares the dataset for model training. During EDA, we discovered that the dataset was imbalanced, so we set the `fix_imbalance` parameter to `True`.


After running `setup()`, we get a table of useful information about its settings and parameters, explained in detail below.
> **Note**: After running the code, we can see the table, given below, by scrolling up in the terminal.

Learn to initialize the classification environment for natural language processing task.

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Initializing the Classification Environment

Classification environment setup