Book Image

Big Data Analytics with Java

Book Image

Big Data Analytics with Java


Overview of this book

This book covers case studies such as sentiment analysis on a tweet dataset, recommendations on a movielens dataset, customer segmentation on an ecommerce dataset, and graph analysis on actual flights dataset. This book is an end-to-end guide to implement analytics on big data with Java. Java is the de facto language for major big data environments, including Hadoop. This book will teach you how to perform analytics on big data with production-friendly Java. This book basically divided into two sections. The first part is an introduction that will help the readers get acquainted with big data environments, whereas the second part will contain a hardcore discussion on all the concepts in analytics on big data. It will take you from data analysis and data visualization to the core concepts and advantages of machine learning, real-life usage of regression and classification using Naïve Bayes, a deep discussion on the concepts of clustering,and a review of simple neural networks on big data using deepLearning4j or plain Java Spark code. This book is a must-have book for Java developers who want to start learning big data analytics and want to use it in the real world.
Table of Contents (21 chapters)
Big Data Analytics with Java
About the Author
About the Reviewers
Customer Feedback
Free Chapter
Big Data Analytics with Java
Ensembling on Big Data
Real-Time Analytics on Big Data


This chapter was action packed on machine learning and its various concepts. We covered a lot of theoretical ground in this chapter by learning what machine learning is, some important real-life use cases, types of machine learning, and the important concepts of machine learning such as how we extract and select features, training our models, selecting our models, and tuning them for performance by using techniques such as training/test set and cross validation. We also learnt how we can run our machine learning models specifically on big data and what Spark has to offer on the machine learning side in terms of an API.

In the next chapter, we will dive into actual machine learning algorithms and we will learn a simple yet powerful and popular linear regression algorithm. We will understand it by using an example case study. After studying linear regression we will study another algorithm logistic regression and we will also try to learn it by using a sample case study.