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Machine Learning with Scala Quick Start Guide

You're reading from Machine Learning with Scala Quick Start Guide

Product type Book

Published in Apr 2019

Publisher Packt

ISBN-13 9781789345070

Pages 220 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Authors (2):

Md. Rezaul Karim

Ajay Kumar N

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Table of Contents (9) Chapters

Preface

Introduction to Machine Learning with Scala

Scala for Regression Analysis

Scala for Learning Classification

Scala for Tree-Based Ensemble Techniques

Scala for Dimensionality Reduction and Clustering

Scala for Recommender System

Introduction to Deep Learning with Scala

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Who this book is for

Machine learning developers looking to learn how to train machine learning models in Scala, without spending too much time and effort, will find this book to be very useful. Some fundamental knowledge of Scala programming and some basics of statistics and linear algebra is all you need to get started with this book.

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Authors (2)

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.

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Ajay Kumar N

Ajay Kumar N has experience in big data, and specializes in cloud computing and various big data frameworks, including Apache Spark and Apache Hadoop. His primary language of choice is Python, but he also has a special interest in functional programming languages such as Scala. He has worked extensively with NumPy, pandas, and scikit-learn, and often contributes to open source projects related to data science and machine learning.

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