E-book details

Machine Learning with Scala Quick Start Guide. Leverage popular machine learning algorithms and techniques and implement them in Scala

Machine Learning with Scala Quick Start Guide. Leverage popular machine learning algorithms and techniques and implement them in Scala

Md. Rezaul Karim

Ebook
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.
  • 1. Introduction to Machine Learning with Scala
  • 2. Scala for Regression Analysis
  • 3. Scala for Learning Classification
  • 4. Scala for Tree-based Ensemble Techniques
  • 5. Scala for Dimensonality Reduction and Clustering
  • 6. Scala for Recommender System
  • 7. Introduction to Deep Learning with Scala
  • Title: Machine Learning with Scala Quick Start Guide. Leverage popular machine learning algorithms and techniques and implement them in Scala
  • Author: Md. Rezaul Karim
  • Original title: Machine Learning with Scala Quick Start Guide. Leverage popular machine learning algorithms and techniques and implement them in Scala
  • ISBN: 9781789345414, 9781789345414
  • Date of issue: 2019-04-30
  • Format: Ebook
  • Item ID: e_2atz
  • Publisher: Packt Publishing