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

## What we learned
Firstly, congratulations on finishing the course! Let’s go over what we learned in the course. We started with a brief introduction to the basic theoretical concepts and then continued with case studies of regression, classification, clustering, and anomaly detection based on their respective modules of the PyCaret library. After that, we focused on using the Streamlit library to develop and deploy machine learning applications. 

> Note: Machine learning is a dynamic and ever-evolving field, and we’ve only scratched the surface of everything there is to know about it.


Regardless, now that we’ve finished this course, we should have a fairly solid understanding of the fundamental machine learning tasks and techniques and the PyCaret library features. We encourage you to consult the [PyCaret documentation](https://pycaret.readthedocs.io/en/latest/) so you can learn more about the modules and functions that we didn’t cover in the course. We also suggest experimenting with datasets that interest you and creating a portfolio of projects that showcase your abilities and experience.




# What we learned
Firstly, congratulations on finishing the course! Let’s go over what we learned in the course. We started with a brief introduction to the basic theoretical concepts and then continued with case studies of regression, classification, clustering, and anomaly detection based on their respective modules of the PyCaret library. After that, we focused on using the Streamlit library to develop and deploy machine learning applications. 

> Note: Machine learning is a dynamic and ever-evolving field, and we’ve only scratched the surface of everything there is to know about it.


Regardless, now that we’ve finished this course, we should have a fairly solid understanding of the fundamental machine learning tasks and techniques and the PyCaret library features. We encourage you to consult the [PyCaret documentation](https://pycaret.readthedocs.io/en/latest/) so you can learn more about the modules and functions that we didn’t cover in the course. We also suggest experimenting with datasets that interest you and creating a portfolio of projects that showcase your abilities and experience.




Congratulations! You have covered the basics of machine learning using PyCaret. Give yourself a pat on your back.

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Closing Thoughts

What we learned