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.

One of the pieces of software required for this book is the Java Development Kit (JDK), which you can get from http://www.oracle.com/technetwork/java/javase/downloads/. JDK is necessary to run and develop applications on the Java platform.

The other major software that you'll need is Leiningen, which you can download and install from http://github.com/technomancy/leiningen. Leiningen is a tool for managing Clojure projects and their dependencies. We will explain how to work with Leiningen in Chapter 1, Working with Matrices.

Throughout this book, we'll use a number of other Clojure and Java libraries, including Clojure itself. Leiningen will take care of the downloading of these libraries for us as required. You'll also need a text editor or an integrated development environment (IDE). If you already have a text editor that you like, you can probably use it. Navigate to http://dev.clojure.org/display/doc/Getting+Started to check the tips and plugins required for using your particular favorite environment. If you don't have a preference, I suggest that you look at using Eclipse with Counterclockwise. There are instructions for getting this set up at http://dev.clojure.org/display/doc/Getting+Started+with+Eclipse+and+Counterclockwise.

In Chapter 9, Large-scale Machine Learning, we also use MongoDB, which can be downloaded and installed from http://www.mongodb.org/.

This book is for programmers or software architects who are familiar with Clojure and want to use it to build machine learning systems. This book does not introduce the syntax and features of the Clojure language (you are expected to be familiar with the language, but you need not be a Clojure expert).

Similarly, although you don't need to be an expert in statistics and coordinate geometry, you should be familiar with these concepts to understand the theory behind the several machine learning techniques that we will discuss. When in doubt, don't hesitate to look up and learn more about the mathematical concepts used in this book.

In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.

Code words in text are shown as follows: "The previously defined probability function requires a single argument to represent the attribute or condition whose probability of occurrence we wish to calculate."

A block of code is set as follows:

(defn predict [coefs X]
  {:pre [(= (count coefs)
            (+ 1 (count X)))]}
  (let [X-with-1 (conj X 1)
        products (map * coefs X-with-1)]
    (reduce + products)))

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

:dependencies [[org.clojure/clojure "1.5.1"]
        [incanter "1.5.2"]
        [clatrix "0.3.0"]
        [net.mikera/core.matrix "0.10.0"]]

Any command-line input or output is written as follows:

$ lein deps

Another simple convention that we use is to always show the Clojure code that's entered in the REPL (read-eval-print-loop) starting with the user> prompt. In practice, this prompt will change depending on the Clojure namespace that we are currently using. However, for simplicity, REPL code starts with the user> prompt, as follows:

user> (every? #(< % 0.0001)
              (map - ols-linear-model-coefs
              (:coefs iris-linear-model))
true

New terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes for example, appear in the text like this: "clicking the Next button moves you to the next screen".

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