Reese Richard M., Reese Jennifer L., Kaluza Bostjan, Kamath Uday, Choppella Krishna. Machine Learning: End-to-End guide for Java developers: Data Analysis, Machine Learning, and Neural Networks simplified

Packt Publishing, 2017. — conv 1276 p. — ASIN B076CRXB76.Detailed coverage on key machine learning topics with an emphasis on both theoretical and practical aspects
Address predictive modeling problems using the most popular machine learning Java libraries
A comprehensive course covering a wide spectrum of topics such as machine learning and natural language through practical use-casesWho This Book Is ForThis course is the right resource for anyone with some knowledge of Java programming who wants to get started with Data Science and Machine learning as quickly as possible. If you want to gain meaningful insights from big data and develop intelligent applications using Java, this course is also a must-have.What You Will LearnUnderstand key data analysis techniques centered around machine learning
Implement Java APIs and various techniques such as classification, clustering, anomaly detection, and more
Master key Java machine learning libraries, their functionality, and various kinds of problems that can be addressed using each of them
Apply machine learning to real-world data for fraud detection, recommendation engines, text classification, and human activity recognition
Experiment with semi-supervised learning and stream-based data mining, building high-performing and real-time predictive models
Develop intelligent systems centered around various domains such as security, Internet of Things, social networking, and more
In Detail
Machine Learning is one of the core area of Artificial Intelligence where computers are trained to self-learn, grow, change, and develop on their own without being explicitly programmed. This course demonstrates complex data extraction and statistical analysis techniques supported by Java, applying various machine learning methods, exploring machine learning sub-domains, and exploring real-world use cases such as recommendation systems, fraud detection, natural language processing, and more, using Java programming. The course begins with an introduction to data science and basic data science tasks such as data collection, data cleaning, data analysis, and data visualization. The next section has a detailed overview of statistical techniques, covering machine learning, neural networks, and deep learning. The next couple of sections cover applying machine learning methods using Java to a variety of chores including classifying, predicting, forecasting, market basket analysis, clustering stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, and deep learning.
The last section highlights real-world test cases such as performing activity recognition, developing image recognition, text classification, and anomaly detection.