This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies.

Machine learning applications perform better with human feedback. Keeping the right people in the loop improves the accuracy of models, reduces errors in data,

This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tr

"This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed co

Practical patterns for scaling machine learning from your laptop to a distributed cluster. Scaling up models from standalone devices to large distributed cluste