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

## Who this course is for
This course is targeted towards Python developers who want to familiarize themselves with the **PyCaret** library and machine learning in general. The content is beginner-friendly, so each lesson assumes no prior knowledge of machine learning techniques and methods. Experienced data scientists and machine learning engineers can also benefit from this course. It will help them acquaint themselves with the low-code approach of PyCaret and improve their workflow. This course will primarily focus on practical coding examples, so we’ll only look at basic explanations of theoretical concepts.





## Prerequisites
This course assumes basic knowledge of Python 3, linear algebra, statistics, and probability theory. Though the mathematics in this course is minimal,  a solid understanding of fundamental mathematical concepts is essential to fully benefit from this course.

# Who this course is for
This course is targeted towards Python developers who want to familiarize themselves with the **PyCaret** library and machine learning in general. The content is beginner-friendly, so each lesson assumes no prior knowledge of machine learning techniques and methods. Experienced data scientists and machine learning engineers can also benefit from this course. It will help them acquaint themselves with the low-code approach of PyCaret and improve their workflow. This course will primarily focus on practical coding examples, so we’ll only look at basic explanations of theoretical concepts.





# Prerequisites
This course assumes basic knowledge of Python 3, linear algebra, statistics, and probability theory. Though the mathematics in this course is minimal,  a solid understanding of fundamental mathematical concepts is essential to fully benefit from this course.

Let’s learn about the course’s outcomes and prerequisites.

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Getting Started

Who this course is for

Prerequisites