Machine Learning: End-to-End guide for Java developers by Krishna Choppella

Key FeaturesDetailed coverage on key machine learning topics with an emphasis on both theoretical and practical aspectsAddress predictive modeling problems using the most popular machine learning Java librariesA comprehensive course covering a wide spectrum of topics such as machine learning and natural language through practical use-casesBook DescriptionMachine 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. In this course, we cover how Java is employed to build powerful machine learning models to address the problems being faced in the world of Data Science. The 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. The course includes premium content from three of our most popular books:Java for Data ScienceMachine Learning in JavaMastering Java Machine LearningOn completion of this course, you will understand various machine learning techniques, different machine learning java algorithms you can use to gain data insights, building data models to analyze larger complex data sets, and incubating applications using Java and machine learning algorithms in the field of artificial intelligence.What you will learnUnderstand key data analysis techniques centered around machine learningImplement Java APIs and various techniques such as classification, clustering, anomaly detection, and moreMaster key Java machine learning libraries, their functionality, and various kinds of problems that can be addressed using each of themApply machine learning to real-world data for fraud detection, recommendation engines, text classification, and human activity recognitionExperiment with semi-supervised learning and stream-based data mining, building high-performing and real-time predictive modelsDevelop intelligent systems centered around various domains such as security, Internet of Things, social networking, and moreAbout the AuthorRichard M. Reese has worked in both industry and academia. For 17 years, he worked in the telephone and aerospace industries, serving in several capacities, including research and development, software development, supervision, and training. He currently teaches at Tarleton State University, where he has the opportunity to apply his years of industry experience to enhance his teaching. Richard has written several Java books and a C Pointer book. He uses a concise and easy-to-follow approach to the topics at hand. His Java books have addressed EJB 3.1, updates to Java 7 and 8, certification, jMonkeyEngine, natural language processing, functional programming, and networks.Jennifer L. Reese studied computer science at Tarleton State University. She also earned her M.Ed. from Tarleton in December 2016. She currently teaches computer science to high-school students. Her research interests include the integration of computer science concepts with other academic disciplines, increasing diversity in computer science courses, and the application of data science to the field of education. She previously worked as a software engineer developing software for county- and district-level government offices throughout Texas. In her free time she enjoys reading, cooking, and traveling—especially to any destination with a beach. She is a musician and appreciates a variety of musical genres.Bostjan Kaluza, PhD, is a researcher in artificial intelligence and machine learning. Bostjan is the chief data scientist at Evolven, a leading IT operations analytics company, focusing on configuration and change management. He works with machine learning, predictive analytics, pattern mining, and anomaly detection to turn data into understandable relevant information and actionable insights. Prior to Evolven, Bostjan served as a senior researcher in the department of intelligent systems at the Jozef Stefan Institute, a leading Slovenian scientific research institution, and led research projects involving pattern and anomaly detection, ubiquitous computing, and multi-agent systems. Bostjan was also a visiting researcher at the University of Southern California, where he studied suspicious and anomalous agent behavior in the context of security applications. Bostjan has extensive experience in Java and Python, and he also lectures on Weka in the classroom. Focusing on machine learning and data science, Bostjan has published numerous articles in professional journals, delivered conference papers, and authored (or contributed to) a number of patents. In 2013, Bostjan published his first book on data science, Instant Weka How-to, by Packt Publishing, exploring how to leverage machine learning using Weka.Dr. Uday Kamath is the chief data scientist at BAE Systems Applied Intelligence. He specializes in scalable machine learning and has spent 20 years in the domain of AML, fraud detection in financial crime, cyber security, and bioinformatics, to name a few.Dr. Kamath is responsible for key products in areas focusing on the behavioral, social networking, and big data machine learning aspects of analytics at BAE AI. He received his PhD at George Mason University, under the able guidance of Dr. Kenneth De Jong, where his dissertation research focused on machine learning for big data and automated sequence mining.Krishna Choppella builds tools and client solutions in his role as a solutions architect for analytics at BAE Systems Applied Intelligence. He has been programming in Java for 20 years. His interests are data science, functional programming, and distributed computing.Table of ContentsGetting Started with Data ScienceData AcquisitionData CleaningData VisualizationStatistical Data Analysis TechniquesMachine LearningNeural NetworksDeep LearningText AnalysisVisual and Audio AnalysisMathematical and Parallel Techniques for Data AnalysisBringing It All TogetherApplied Machine Learning Quick StartJava Libraries and Platforms for Machine LearningBasic Algorithms – Classification, Regression, and ClusteringCustomer Relationship Prediction with EnsemblesAffinity AnalysisRecommendation Engine with Apache MahoutFraud and Anomaly DetectionImage Recognition with Deeplearning4jActivity Recognition with Mobile Phone SensorsText Mining with Mallet – Topic Modeling and Spam DetectionWhat is Next?ReferencesMachine Learning ReviewPractical Approach to Real-World Supervised LearningUnsupervised Machine Learning TechniquesSemi-Supervised and Active LearningReal-Time Stream Machine LearningProbabilistic Graph ModelingDeep LearningText Mining and Natural Language ProcessingBig Data Machine Learning – The Final FrontierLinear AlgebraProbability