About the Book
The field of machine learning has grown substantially in the past years due to technological and scientific advancements. Data scientists and machine learning engineers are among the best paid professionals in the modern job market, and demand for associated skills is extremely high. PyCaret is a low-code machine learning Python library that is easy to use, thus making it accessible to beginners. This book provides hands-on tutorials for each of the main PyCaret modules, such as classification, regression, clustering, anomaly detection and natural language processing. Furthermore, the book will teach you how to develop machine learning applications based on the Streamlit library, as well as deploy them on the cloud. Apart from beginners, this book can also be valuable to experts who want to familiarize themselves with the PyCaret library and its features.
- This book will be updated with additional chapters in the following months. Customers who buy it now are getting lifetime access to all future updates!
- The upcoming chapter will cover the Time Series Forecasting module of PyCaret.
- The book has been optimized for the PDF file format, so the Epub and Mobi versions may have some issues.
- I am donating 10% of the book royalties to Tree-Nation, a non-profit organization that allows citizens and companies to plant trees all around the world and offset their CO2 emissions.
About the Author
Giannis Tolios is a data scientist who is passionate about expanding his knowledge and evolving as a professional. He has collaborated with numerous companies worldwide as a freelancer, and completed projects related to machine learning, time series forecasting, data visualization and others. Giannis also enjoys writing about data science at established websites such as Towards Data Science and Analytics Vidhya. Giannis strongly believes that technology should be used for good, and is constantly looking for new ways to help mitigate challenges like climate change and economic inequality, by using data science. If you want to learn more about Giannis, you can visit his personal website or follow him on LinkedIn where he’s regularly posting content about data science and other topics.