Interpretable Machine Learning with Python
By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning.
Interpretable Machine Learning
This book is about making machine learning models and their decisions interpretable.
Explainable AI with Python
This book provides a full presentation of the current concepts and available techniques to make “machine learning” systems more explainable.
Explanatory Model Analysis
This book presents a collection of model agnostic methods that may be used for any black-box model together with real-world applications to classification and regression problems.
Thoughtful Machine Learning with Python
Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext.
Machine Learning for Ecology and Sustainable Natural Resource Management
Currently, many ecologists do not utilize machine learning as a part of the scientific process. This volume highlights how machine learning techniques can complement the traditional methodologies currently applied in this field.
Hands-On Explainable AI (XAI) with Python
Some of the potential readers of this book include: Professionals who already use Python for as data science, machine learning, research, and analysisData analysts and data scientists who want an introduction into explainable AI tools and ...
Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits
This book serves as a practical guide for anyone looking to provide hands-on machine learning solutions with scikit-learn and Python toolkits.
Hands-On Machine Learning with R
This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, ...
Introduction to Machine Learning with Python
With this book, you’ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data ...
