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 covered are self-training, generative models, transductive SVMs, co-training, active learning, and multi-view learning. The case study involves both learning systems and is performed on the real-world UCI Breast Cancer Wisconsin dataset. The tools introduced are JKernelMachines ,KEEL and JCLAL.
Chapter 5, Real-Time Stream Machine Learning, covers data streams in real-time present unique circumstances for the problem of learning from data. This chapter broadly covers the need for stream machine learning and applications, supervised stream learning, unsupervised cluster stream learning, unsupervised outlier learning, evaluation techniques in stream learning, and metrics used for evaluation. A detailed case study is given at the end of the chapter to illustrate the use of the MOA framework. The dataset used is Electricity (ELEC).
Chapter 6, Probabilistic Graph Modeling, shows that many real-world problems can be effectively represented by encoding complex joint probability distributions over multi-dimensional spaces. Probabilistic graph models provide a framework to represent, draw inferences, and learn effectively in such situations. The chapter broadly covers probability concepts, PGMs, Bayesian networks, Markov networks, Graph Structure Learning, Hidden Markov Models, and Inferencing. A detailed case study on a real-world dataset is performed at the end of the chapter. The tools used in this case study are OpenMarkov and WEKA's Bayes network. The dataset is UCI Adult (Census Income).
Chapter 7, Deep Learning, If there is one super-star of machine learning in the popular imagination today it is deep learning, which has attained a dominance among techniques used to solve the most complex AI problems. Topics broadly covered are neural networks, issues in neural networks, deep belief networks, restricted Boltzman machines, convolutional networks, long short-term memory units, denoising autoencoders, recurrent networks, and others. We present a detailed case study showing how to implement deep learning networks, tuning the parameters and performing learning. We use DeepLearning4J with the MNIST image dataset.
Chapter 8, Text Mining and Natural Language Processing, details the techniques, algorithms, and tools for performing various analyses in the field of text mining. Topics broadly covered are areas of text mining, components needed for text mining, representation of text data, dimensionality reduction techniques, topic modeling, text clustering, named entity recognition, and deep learning. The case study uses real-world unstructured text data (the Reuters-21578 dataset) highlighting topic modeling and text classification; the tools used are MALLET and KNIME.
Chapter 9, Big Data Machine Learning – the Final Frontier, discusses some of the most important challenges of today. What learning options are available when data is either big or available at a very high velocity? How is scalability handled? Topics covered are big data cluster deployment frameworks, big data storage options, batch data processing, batch data machine learning, real-time machine learning frameworks, and real-time stream learning. In the detailed case study for both big data batch and real-time we select the UCI Covertype dataset and the machine learning libraries H2O, Spark MLLib and SAMOA.
