Scala Machine Learning Projects: Build real-world machine learning and deep learning projects with Scala

by Md. Rezaul Karim

Overview

Machine learning has had a huge impact on academia and industry by turning data into actionable information. Scala has seen a steady rise in adoption over the past few years, especially in the fields of data science and analytics. This book is for data scientists, data engineers, and deep learning enthusiasts who have a background in complex numerical computing and want to know more hands-on machine learning application development.
If you're well versed in machine learning concepts and want to expand your knowledge by delving into the practical implementation of these concepts using the power of Scala, then this book is what you need! Through 11 end-to-end projects, you will be acquainted with popular machine learning libraries such as Spark ML, H2O, DeepLearning4j, and MXNet.
At the end, you will be able to use numerical computing and functional programming to carry out complex numerical tasks to develop, build, and deploy research or commercial projects in a production-ready environment.

Product Details

ISBN-13:	9781788471473
Publisher:	Packt Publishing
Publication date:	01/31/2018
Sold by:	Barnes & Noble
Format:	eBook
Pages:	470
File size:	30 MB
Note:	This product may take a few minutes to download.

About the Author

Md. Rezaul Karim is 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. Before that, he worked as a Lead Engineer at Samsung Electronics, Korea.

He has 9 years of R&D experience with C++, Java, R, Scala, and Python. He has published several research papers concerning bioinformatics, big data, and deep learning. He has practical working experience with Spark, Zeppelin, Hadoop, Keras, Scikit-Learn, TensorFlow, DeepLearning4j, MXNet, and H2O.

Table of Contents

Table of Contents

1. Analyzing Insurance Severity Claim
2. Analyzing Outgoing Customers through Churn Prediction
3. High Frequency Bitcoin Price Prediction from Historical and Live Data
4. Population Scale Clustering and Ethnicity Analysis
5. Topic Modelling in NLP: A Better Insight to Large-Scale Texts
6. Model-based Movie Recommendation Engine
7. Deep Reinforcement Learning using Markov Decision Process (MDP)
8. Using Deep Belief Networks in Bank Marketing
9. Fraud Analytics using Autoencoders and Anomaly Detection
10. Human Activity Recognition using RNN
11. Image Classification using CNN