Practical Guide to Applied Conformal Prediction in Python

Take your machine learning skills to the next level by mastering the best framework for uncertainty quantification - Conformal Prediction

Book Description
In the rapidly evolving landscape of machine learning, the ability to accurately quantify uncertainty is pivotal. "Practical Guide to Applied Conformal Prediction in Python" addresses this need by offering an in-depth exploration of Conformal Prediction, a cutting-edge framework set to revolutionize uncertainty management in various ML applications. Embark on a comprehensive journey through Conformal Prediction, exploring its fundamentals and practical applications in binary classification, regression, time series forecasting, imbalanced data, 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. Practical examples in Python using real-world datasets reinforce intuitive explanations, ensuring you acquire a robust understanding of this modern framework for uncertainty quantification. This guide is a beacon for mastering Conformal Prediction in Python, providing a blend of theory and practical application. It serves as a comprehensive toolkit to enhance machine learning skills, catering to professionals from data scientists to ML engineers.

Table of Contents
Part 1: Introduction
Chapter 1: Introducing Conformal Prediction
Chapter 2: Overview of Conformal Prediction
Part 2: Conformal Prediction Framework
Chapter 3: Fundamentals of Conformal Prediction
Chapter 4: Validity and Efficiency of Conformal Prediction
Chapter 5: Types of Conformal Predictors
Part 3: Applications of Conformal Prediction
Chapter 6: Conformal Prediction for Classification
Chapter 7: Conformal Prediction for Regression
Chapter 8: Conformal Prediction for Time Series and Forecasting
Chapter 9: Conformal Prediction for Computer Vision
Chapter 10: Conformal Prediction for Natural Language Processing
Part 4: Advanced Topics
Chapter 11: Handling Imbalanced Data
Chapter 12: Multi-Class Conformal Prediction
Index
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Authors
Valeriy Manokhin is the leading expert in the field of machine learning and Conformal Prediction. He holds a Ph.D.in Machine Learning from Royal Holloway, University of London. His doctoral work was supervised by the creator of Conformal Prediction, Vladimir Vovk, and focused on developing new methods for quantifying uncertainty in machine learning models. Valeriy has published extensively in leading machine learning journals, and his Ph.D. dissertation 'Machine Learning for Probabilistic Prediction' is read by thousands of people across the world. He is also the creator of "Awesome Conformal Prediction," the most popular resource and GitHub repository for all things Conformal Prediction.