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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

We’ll now perform EDA on our data. As mentioned earlier, EDA is a method that helps us understand the dataset properties by using descriptive statistics and visualization. It is an important part of every machine learning or data science project because it’s essential that we understand the data set before we utilize it.

Let’s learn how to perform exploratory data analysis for regression on our dataset and plot histograms, bar charts, and scatter plots.

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Exploratory Data Analysis