Successfully leverage advanced machine learning techniques using the Clojure ecosystem.
Clojure for Machine Learning is an introduction to machine learning techniques and algorithms. This book demonstrates how you can apply these techniques to real-world problems using the Clojure programming language.
It explores many machine learning techniques and also describes how to use Clojure to build machine learning systems. This book starts off by introducing the simple machine learning problems of regression and classification. It also describes how you can implement these machine learning techniques in Clojure. The book also demonstrates several Clojure libraries, which can be useful in solving machine learning problems.
Clojure for Machine Learning familiarizes you with several pragmatic machine learning techniques. By the end of this book, you will be fully aware of the Clojure libraries that can be used to solve a given machine learning problem.
What this book covers
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 back-propagation 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.
Author: Akhil Wali
Clojure for Machine Learning
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