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Interpretable Machine Learning 2/eMachine learning has great potential for improving products, processes and research. But computers usually do not explain their predictions which is a barrier to the adoption of machine learning. This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, inter pretable models such as decision trees, decision rules and linear regression. The focus

You'll get adaptable code samples that you can add to almost any project--and you'll learn more about JavaScript in the process

using PHP 7

Daniel Vaughan is head of data at Clip

You'll understand how your storage solution choice determines the robustness and performance of the data pipeline

and reusable code in no time

If you are ready to dive into the MapReduce framework for processing large datasets

In this best-of-breed study guide

single-file programs—roughly 90% of the Perl programs in use today

Walk through key AI methods like decision trees

Ola criticizing Uber over 'predatory pricing' in India

A new book designed for SysAdmins

"Today's market for mobile apps goes beyond the iPhone to include BlackBerry