Machine learning has a vast variety of applications in computing. Software systems that use machine learning techniques tend to provide their users with a better user experience. With cloud data becoming more relevant these days, developers will eventually build more intelligent systems that simplify and optimize any routine task for their users.
This book will introduce several machine learning techniques and also describe how we can leverage these techniques in the Clojure programming language.
Clojure is a dynamic and functional programming language built on the Java Virtual Machine (JVM). It's important to note that Clojure is a member of the Lisp family of languages. Lisp played a key role in the artificial intelligence revolution that took place during the 70s and 80s. Unfortunately, artificial intelligence lost its spark in the late 80s. Lisp, however, continued to evolve, and several dialects of Lisp have been concocted throughout the ages. Clojure is a simple and powerful dialect of Lisp that was first released in 2007. At the time of writing this book, Clojure is one of the most rapidly growing programming languages for the JVM. It currently supports some of the most advanced language features and programming methodologies out there, such as optional typing, software transactional memory, asynchronous programming, and logic programming. The Clojure community is known to mesmerize developers with their elegant and powerful libraries, which is yet another compelling reason to use Clojure.
Machine learning techniques are based on statistics and logic-based reasoning. In this book, we will focus on the statistical side of machine learning. Most of these techniques are based on principles from the artificial intelligence revolution. Machine learning is still an active area of research and development. Large players from the software world, such as Google and Microsoft, have also made significant contributions to machine learning. More software companies are now realizing that applications that use machine learning techniques provide a much better experience to their users.
Although there is a lot of mathematics involved in machine learning, we will focus more on the ideas and practical usage of these techniques, rather than concentrating on the theory and mathematical notations used by these techniques. This book seeks to provide a gentle introduction to machine learning techniques and how they can be used in Clojure.
Chapter 1, Working with Matrices, explains matrices and the basic operations on matrices that are useful for implementing the machine learning algorithms.
Chapter 2, Understanding Linear Regression, introduces linear regression as a form of supervised learning. We will also discuss the gradient descent algorithm and the ordinary least-squares (OLS) method for fitting the linear regression models.
Chapter 3, Categorizing Data, covers classification, which is another form of supervised learning. We will study the Bayesian method of classification, decision trees, and the k-nearest neighbors algorithm.
Chapter 4, Building Neural Networks, explains artificial neural networks (ANNs) that are useful in the classification of nonlinear data, and describes a few ANN models. We will also study and implement the backpropagation algorithm that is used to train an ANN and describe self-organizing maps (SOMs).
Chapter 5, Selecting and Evaluating Data, covers evaluation of machine learning models. In this chapter, we will discuss several methods that can be used to improve the effectiveness of a given machine learning model. We will also implement a working spam classifier as an example of how to build machine learning systems that incorporate evaluation.
Chapter 6, Building Support Vector Machines, covers support vector machines (SVMs). We will also describe how SVMs can be used to classify both linear and nonlinear sample data.
Chapter 7, Clustering Data, explains clustering techniques as a form of unsupervised learning and how we can use them to find patterns in unlabeled sample data. In this chapter, we will discuss the K-means and expectation maximization (EM) algorithms. We will also explore dimensionality reduction.
Chapter 8, Anomaly Detection and Recommendation, explains anomaly detection, which is another useful form of unsupervised learning. We will also discuss recommendation systems and several recommendation algorithms.
Chapter 9, Large-scale Machine Learning, covers techniques that are used to handle a large amount of data. Here, we explain the concept of MapReduce, which is a parallel data-processing technique. We will also demonstrate how we can store data in MongoDB and how we can use the BigML cloud service to build machine learning models.
Appendix, References, lists all the bibliographic references used throughout the chapters of this book.