Machine Learning with Scala Quick Start Guide

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  • Get acquainted with JVM-based machine learning libraries for Scala such as Spark ML and Deeplearning4j
  • Learn RDDs, DataFrame, and Spark SQL for analyzing structured and unstructured data
  • Understand supervised and unsupervised learning techniques with best practices and pitfalls
  • Learn classification and regression analysis with linear regression, logistic regression, Naïve Bayes, support vector machine, and tree-based ensemble techniques
  • Learn effective ways of clustering analysis with dimensionality reduction techniques
  • Learn recommender systems with collaborative filtering approach
  • Delve into deep learning and neural network architectures

Scala is a highly scalable integration of object-oriented nature and functional programming concepts that make it easy to build scalable and complex big data applications. This book is a handy guide for machine learning developers and data scientists who want to develop and train effective machine learning models in Scala.

The book starts with an introduction to machine learning, while covering deep learning and machine learning basics. It then explains how to use Scala-based ML libraries to solve classification and regression problems using linear regression, generalized linear regression, logistic regression, support vector machine, and Naïve Bayes algorithms.

It also covers tree-based ensemble techniques for solving both classification and regression problems. Moving ahead, it covers unsupervised learning techniques, such as dimensionality reduction, clustering, and recommender systems. Finally, it provides a brief overview of deep learning using a real-life example in Scala.

  • Construct and deploy machine learning systems that learn from your data and give accurate predictions
  • Unleash the power of Spark ML along with popular machine learning algorithms to solve complex tasks in Scala.
  • Solve hands-on problems by combining popular neural network architectures such as LSTM and CNN using Scala with DeepLearning4j library
Page Count 220
Course Length 6 hours 36 minutes
ISBN 9781789345070
Date Of Publication 29 Apr 2019


Md. Rezaul Karim

Md. Rezaul Karim is a researcher, author, and data science enthusiast with a strong computer science background, coupled with 10 years of research and development experience in machine learning, deep learning, and data mining algorithms to solve emerging bioinformatics research problems by making them explainable. He is passionate about applied machine learning, knowledge graphs, and explainable artificial intelligence (XAI). Currently, he is working as a research scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Germany. Before joining FIT, he worked as a researcher at the Insight Centre for Data Analytics, Ireland. Previously, he worked as a lead software engineer at Samsung Electronics, Korea.