Closing Thoughts

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

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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 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.

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