Over 80 recipes to simplify machine learning model implementations with Spark
About This Book
- Solve the day-to-day problems of data science with Spark
- This unique cookbook consists of exciting and intuitive numerical recipes
- Optimize your work by acquiring, cleaning, analyzing, predicting, and visualizing your data
Who This Book Is For
This book is for Scala developers with a fairly good exposure to and understanding of machine learning techniques, but lack practical implementations with Spark. A solid knowledge of machine learning algorithms is assumed, as well as hands-on experience of implementing ML algorithms with Scala. However, you do not need to be acquainted with the Spark ML libraries and ecosystem.
What You Will Learn
- Get to know how Scala and Spark go hand-in-hand for developers when developing ML systems with Spark
- Build a recommendation engine that scales with Spark
- Find out how to build unsupervised clustering systems to classify data in Spark
- Build machine learning systems with the Decision Tree and Ensemble models in Spark
- Deal with the curse of high-dimensionality in big data using Spark
- Implement Text analytics for Search Engines in Spark
- Streaming Machine Learning System implementation using Spark
In Detail
Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability, and optimization. Learning about algorithms enables a wide range of applications, from everyday tasks such as product recommendations and spam filtering to bleeding edge applications such as self-driving cars and personalized medicine. You will gain hands-on experience of applying these principles using Apache Spark, a cluster computing system well suited for large-scale machine learning tasks.
This book begins with a quick overview of setting up the necessary IDEs to facilitate the execution of code examples that will be covered. It also highlights some key issues developers face while thinking about Scala for machine learning and during the switch over to Spark. We progress by uncovering the various Spark APIs and the implementation of ML algorithms with developing classification systems, recommendation engines, clustering and learning systems. Towards the final chapters, we’ll focus on building high-end applications and explain various unsupervised methodologies and challenges to tackle when implementing with big data ML systems.
