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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

## Histogram of numeric features
We begin the EDA section of the project by visualizing the distribution of numeric features. We’ll use the `hist()` pandas function to accomplish this task.

# Histogram of numeric features
We begin the EDA section of the project by visualizing the distribution of numeric features. We’ll use the `hist()` pandas function to accomplish this task.

Learn how to perform exploratory data analysis for anomaly detection on our dataset and plot data on histogram, box plot, bar plot, heatmap plot, 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

Histogram of numeric features