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Explore the world of lightning fast Clojure apps with asynchronous channels, logic, reactive programming, and more About This Book Discover Clojure's features and advantages and use them in your existing projects Explore lesser-known and more advanced features, constructs, and methodologies such as

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Chapter 1, Machine Learning Review, is a refresher of basic concepts and techniques that the reader would have learned from Packt's Learning Machine Learning in Java or a similar text. This chapter is a review of concepts such as data, data transformation, sampling and bias, features and their importance, supervised learning, unsupervised learning, big data learning, stream and real-time learning, probabilistic graphic models, and semi-supervised learning. Chapter 2, Practical Approach to Real-World Supervised Learning, cobwebs dusted, dives straight into the vast field of supervised learning and the full spectrum of associated techniques. We cover the topics of feature selection and reduction, linear modeling, logistic models, non-linear models, SVM and kernels, ensemble learning techniques such as bagging and boosting, validation techniques and evaluation metrics, and model selection. Using WEKA and RapidMiner, we carry out a detailed case study, going through all the steps from data analysis to analysis of model performance. As in each of the other chapters, the case study is presented as an example to help the reader understand how the techniques introduced in the chapter are applied in real life. The dataset used in the case study is UCI HorseColic. Chapter 3, Unsupervised Machine Learning Techniques, presents many advanced methods in clustering and outlier techniques, with applications. Topics covered are feature selection and reduction in unsupervised data, clustering algorithms, evaluation methods in clustering, and anomaly detection using statistical, distance, and distribution techniques. At the end of the chapter, we perform a case study for both clustering and outlier detection using a real-world image dataset, MNIST. We use the Smile API to do feature reduction and ELKI for learning. Chapter 4, Semi-supervised Learning and Active Learning, gives details of algorithms and techniques for learning when only a small amount labeled data is present. Topics

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The super-fast evolution of the JDK between versions 8 and 12 has increased the learning curve of modern Java, therefore has increased the time needed for placing developers in the Plateau of Productivity. Its new features and concepts can be adopted to solve a variety of modern-day problems. This book enables you to adopt an objective approach to common problems by explaining the correct practices and decisions with respect to complexity, performance, readability, and more. Java Coding Problem