This book is about making machine learning models and their decisions interpretable. After
exploring the concepts of interpretability, you will learn about simple, interpretable models …

Online social media drive the growth of unstructured text data. Many marketing applications
require structuring this data at scales non-accessible to human coding, eg, to detect …

Recent Weak Supervision (WS) approaches have had widespread success in easing the
bottleneck of labeling training data for machine learning by synthesizing labels from multiple …

Cybercriminals use social media platforms to disseminate spam, misleading facts, fake
news, and malicious links. Blocking such deceptive social media spam is essential …

High dimensionality problem is an important concern for short text classification due to its
effect on computational cost and accuracy of classifiers. Also, short text data, besides being …

Weak supervision is a popular method for building machine learning models without relying
on ground truth annotations. Instead, it generates probabilistic training labels by estimating …

Background: With digital transformation and growing social media usage, kids spend
considerable time on the web, especially watching videos on YouTube. YouTube is a source …

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 …

A large number of the most-subscribed YouTube channels target children of very young age.
Hundreds of toddler-oriented channels on YouTube feature inoffensive, well produced, and …

In many applications labeled data is not readily available, and needs to be collected via pain-
staking human supervision. We propose a rule-exemplar method for collecting human …