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About Practical Guide to Applied Conformal Prediction in Python: Learn and apply the best uncertainty frameworks to your industry applications
Take your machine learning skills to the next level by mastering the best framework for uncertainty quantification – Conformal Prediction Embark on an insightful journey with ‘Practical Guide to Applied Conformal Prediction in Python’, a comprehensive resource that equips you with the latest techniques to quantify uncertainty in machine learning and computer vision models effectively. This book covers a wide array of real-world applications, including Conformal Prediction for forecasting, computer vision, and NLP, as well as advanced examples for handling imbalanced data and multi-class classification problems. These practical case studies will enable you to apply your newfound knowledge to various industry scenarios. Designed for data scientists, analysts, machine learning engineers, and industry professionals, this book caters to different skill levels – making it an ideal resource for both beginners and experienced practitioners. Delve into the latest Conformal Prediction techniques and elevate your machine learning expertise. If you’re eager to manage uncertainty in industry applications using Python, ‘Practical Guide to Applied Conformal Prediction in Python’ is the ultimate guide for you. Order your copy today and propel your career to new heights! This book is for people interested in Conformal Prediction – data scientists, machine learning engineers, academics, researchers, software developers, students, data analysts, statisticians.
Detailed Information
| Author: | Valeriy Manokhin |
|---|---|
| Publication Year: | 2023 |
| ISBN: | 9781805122760 |
| Language: | English |
| File Size: | 7 |
| Format: | |
| Price: | FREE |
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