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Master Conformal Prediction, a fast-growing ML framework, with Python applications

Explore cutting-edge methods to measure and manage uncertainty in industry applications

Understand how Conformal Prediction differs from traditional machine learning

Description

In the rapidly evolving landscape of machine learning, the ability to accurately quantify uncertainty is pivotal. The book addresses this need by offering an in-depth exploration of Conformal Prediction, a cutting-edge framework to manage uncertainty in various ML applications. Learn how Conformal Prediction excels in calibrating classification models, produces well-calibrated prediction intervals for regression, and resolves challenges in time series forecasting and imbalanced data. Discover specialised applications of conformal prediction in cutting-edge domains like computer vision and NLP. Each chapter delves into specific aspects, offering hands-on insights and best practices for enhancing prediction reliability. The book concludes with a focus on multi-class classification nuances, providing expert-level proficiency to seamlessly integrate Conformal Prediction into diverse industries. With practical examples in Python using real-world datasets, expert insights, and open-source library applications, you will gain a solid understanding of this modern framework for uncertainty quantification. By the end of this book, you will be able to master Conformal Prediction in Python with a blend of theory and practical application, enabling you to confidently apply this powerful framework to quantify uncertainty in diverse fields.

What you will learn

The fundamental concepts and principles of conformal prediction

Learn how conformal prediction differs from traditional ML methods

Apply real-world examples to your own industry applications

Explore advanced topics - imbalanced data and multi-class CP

Dive into the details of the conformal prediction framework

Boost your career as a data scientist, ML engineer, or researcher

Learn to apply conformal prediction to forecasting and NLP

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Frequently bought together

Causal Inference and Discovery in Python

$53.99

Interpretable Machine Learning with Python

$49.99

Practical Guide to Applied Conformal Prediction in Python

$49.99

Total $ 153.97

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S M Feb 13, 2024

Having delved deep into "Practical Guide to Applied Conformal Prediction in Python" by Valery Manokhin, I'm compelled to share my enthusiasm for this standout resource in the machine learning community. This book is a comprehensive exploration of Conformal Prediction (CP), a topic of paramount importance for anyone keen on elevating their data science and machine learning projects to the next level.The breadth and depth of topics covered in this book are truly impressive. Manokhin starts with the fundamental concepts of CP, ensuring readers understand the theoretical underpinnings before diving into its practical applications. What sets this book apart is its holistic approach, encompassing a wide array of applications from binary classification and regression to the more complex realms of time series forecasting, computer vision, and natural language processing (NLP).The chapters on time series forecasting and computer vision are particularly enlightening, showcasing CP's versatility and power in handling diverse and challenging datasets. The book goes beyond the basics, delving into nuanced topics like imbalanced data and multi-class CP, areas often overlooked in other texts. This depth ensures that readers are not only equipped with theoretical knowledge but also with the practical skills to apply CP in real-world scenarios.Practical examples peppered throughout the book, all in Python, reinforce the material, allowing readers to see CP in action. These examples are not just academic exercises; they are drawn from real-world datasets, making the lessons learned directly applicable to one's own industry projects.Moreover, the focus on enhancing prediction reliability through CP is timely and critical. In an era where data-driven decision-making is paramount, the ability to accurately quantify and communicate prediction uncertainty is a game-changer. This book empowers readers to do just that, boosting their confidence in the models they build and deploy.In conclusion, "Practical Guide to Applied Conformal Prediction in Python" is an invaluable asset for data scientists, ML engineers, academics, and anyone interested in advancing their understanding of uncertainty quantification in machine learning. Whether you're a novice seeking to learn about CP or a seasoned practitioner aiming to refine your skills, this book is a must-have. It's not just a guide; it's a comprehensive toolkit that will undoubtedly enhance your machine learning endeavors.

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Deepak Deokar Jan 09, 2024

I recently had the pleasure of diving into "Practical Guide to Applied Conformal Prediction in Python," and I must say, this book is an absolute gem for anyone interested in mastering the art of uncertainty quantification and predictive modeling. The author has done an exceptional job in providing a comprehensive guide that is both informative and practical.This book takes you on a journey through the world of conformal prediction, a powerful technique for estimating the uncertainty of machine learning models. It not only covers the theory behind conformal prediction but also provides hands-on examples and code snippets in Python, making it accessible even for those with limited experience in the field.

Daniel Dec 26, 2023

The book covers a very essential topic imo. It is well structured and easy to follow with a lot of intuitive examples (including its python implementation in corresponding notebooks). I also like that the original papers are always directly referenced in the text. This encourages you to delve even deeper into the topic.TLDR: Excellent overview with hands-on examples!

D. Burakov Apr 21, 2024

Coming from a high-risk ML area (finance/banking DS), I found the book touching upon very important concepts useful to practitioners in my space where uncertainty = risk. It gives a very intuitive start to the framework discussing the commonly used scores based on real-world examples of binary and multi-class classification and applies the theory to a variety of use-cases. The book is easy to follow and has excellent examples in Python.The two chapters on Computer Vision and NLP are very well composed and allow one to see the framework from different angles. I particularly enjoyed the section on pitfalls in calibration using conventional tools and Venn-ABERS predictors.The next editions of the book could greatly benefit from expanding the "Comparing calibration methods" section, for example, a summary comparison of some binning and scaling techniques (e.g., BBQ, Beta calibration, temperature scaling among others) vs Venn-ABERS would enhance the understanding of the utility of conformal prediction in the context of probability calibration in the Guide.Will definitely return many times to this book by Valery in my journey into the conformal prediction!

Thomas D. Kocar May 18, 2024

Where other theoretical frameworks for uncertainty quantification fail, conformal prediction (CP) shines. In a world, where solving complex engineering problems is becoming ever so important, relying on frameworks that can not provide validity guarentees is a recipe for failure. It comes as no surprise that the current artificial intelligence (AI) boom comes with a rekindled interest in CP.Valeriy Manokhin does an excellent job in introducing CP to anyone new to the field. After a brief and intuitive overview, he goes into more detail and explains the theoretical background and math behind each CP application. In addition, he provides instructions on how to implement CP in your own project and even highlights respective software libraries.For me, this is the gold standard for getting started with CP.

Practical Guide to Applied Conformal Prediction in Python: Learn and apply the best uncertainty frameworks to your industry applications

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