Large Scale Machine Learning with Spark-Packt Publishing(2016).p...

Machine learning, at its core, is concerned with algorithms that transform raw data into information into actionable intelligence. This fact makes machine learning well suited to the predictive analytics of Big Data. Without machine learning, therefore, it would be nearly impossible to keep up with these massive streams of information altogether. Spark, which is relatively a new and emerging technology, provides big data engineers and data scientists a powerful response and a unified engine that is both faster and easy to use. This allows learners from numerous areas to solve their machine learning problems interactively and at much greater scale. The book is designed to enable data scientists, engineers, and researchers to develop and deploy their machine learning applications at scale so that they can learn how to handle large data clusters in data intensive environments to build powerful machine learning models. The contents of the books have been written in a bottom-up approach from Spark and ML basics, exploring data with feature engineering, building scalable ML pipelines, tuning and adapting them through for the new data and problem types, and finally, model building to deployment. To clarify more, we have provided the chapters outline in such a way that a new reader with a minimum of knowledge of machine learning and programming with Spark will be able to follow the examples and move towards some real-life machine learning problems and their solutions.