Data in today’s world is the new black gold which is growing exponentially. This growth can be attributed to the growth of existing data, and new data in a structured and unstructured format from multiple sources such as social media, Internet, documents and the Internet of Things. The flow of data must be collected, processed, analyzed, and finally presented in real time to ensure that the consumers of the data are able to take informed decisions in today’s fast-changing environment. Machine learning techniques are applied to the data using the context of the problem to be solved to ensure that fast arriving and complex data can be analyzed in a scientific manner using statistical techniques. Using machine learning algorithms that iteratively learn from data, hidden patterns can be discovered. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt and learn to produce reliable decisions from new data sets.
We will start by introducing the various topics of machine learning, that will be covered in the book. Based on real-world challenges, we explore each of the topics under various chapters, such as Classification, Clustering, Model Selection and Regularization, Nonlinearity, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Structured Prediction, Neural Networks, Deep Learning, and finally the case studies. The algorithms have been developed using R as the programming language. This book is friendly for beginners in R, but familiarity with R programming would certainly be helpful for playing around with the code.
You will learn how to make informed decisions about the type of algorithms you need to use and how to implement these algorithms to get the best possible results. If you want to build versatile applications that can make sense of images, text, speech, or some other form of data, this book on machine learning will definitely come to your rescue!
Chapter 1, Introduction to Machine Learning, covers various concepts about machine learning. This chapter makes the reader aware of the various topics we shall be covering in the book.
Chapter 2, Classification, covers the following topics and algorithms: discriminant function analysis, multinomial logistic regression, Tobit regression, and Poisson regression.
Chapter 3, Clustering, covers the following topics and algorithms: hierarchical clustering, binary clustering, and k-means clustering.
Chapter 4, Model Selection and Regularization, covers the following topics and algorithms: shrinkage methods, dimension reduction methods, and principal component analysis.
Chapter 5, Nonlinearity, covers the following topics and algorithms: generalized additive models, smoothing splines, local regression.
Chapter 6, Supervised Learning, covers the following topics and algorithms: decision tree learning, Naive Bayes, random forest, support vector machine, and stochastic gradient descent.
Chapter 7, Unsupervised Learning, covers the following topics and algorithms: self-organizing map, and vector quantization.
Chapter 8, Reinforcement Learning, covers the following topics and algorithms: Markov chains, and Monte Carlo simulations.
Chapter 9, Structured Prediction, covers the following topic and algorithms: hidden Markov models.
Chapter 10, Neural Networks, covers the following topic and algorithms: neural networks.
Chapter 11, Deep Learning, covers the following topic and algorithms: recurrent neural networks.
Chapter 12, Case Study - Exploring World Bank Data, covers World Bank data analysis.
Chapter 13, Case Study - Pricing Reinsurance Contracts, covers pricing reinsurance contracts.
Chapter 14, Case Study - Forecast of Electricity Consumption, covers forecasting electricity consumption.
This book is focused on building machine learning-based applications in R. We have used R to build various solutions. We focused on how to utilize various R libraries and functions in the best possible way to overcome real-world challenges. We have tried to keep all the code as friendly and readable as possible. We feel that this will enable our readers to easily understand the code and readily use it in different scenarios.
This book is for students and professionals working in the fields of statistics, data analytics, machine learning, and computer science, or other professionals who want to build real-world machine learning-based applications. This book is friendly to R beginners, but being familiar with R would be useful for playing around with the code. The will also be useful for experienced R programmers who are looking to explore machine learning techniques in their existing technology stacks.
In this book, you will find headings that appear frequently (Getting ready and How to do it).
To give clear instructions on how to complete a recipe, we use these sections as follows:
This section tells you what to expect in the recipe, and describes how to set up any software or any preliminary settings required for the recipe.
In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "We will be saving the data to the fitbit_details frame:"
Any command-line input or output is written as follows:
install.packages("ggplot2")
New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Monte Carlo v/s Market n Zero Rates"
Feedback from our readers is always welcome. Let us know what you think about this book-what you liked or disliked. Reader feedback is important for us as it helps us develop titles that you will really get the most out of.
To send us general feedback, simply e-mail [email protected], and mention the book's title in the subject of your message.
If there is a topic that you have expertise in and you are interested in either writing or contributing to a book, see our author guide at www.packtpub.com/authors .
Now that you are the proud owner of a Packt book, we have a number of things to help you to get the most from your purchase.
You can download the example code files for this book from your account at http://www.packtpub.com. If you purchased this book elsewhere, you can visit http://www.packtpub.com/support and register to have the files e-mailed directly to you.
You can download the code files by following these steps:
- Log in or register to our website using your e-mail address and password.
- Hover the mouse pointer on the SUPPORT tab at the top.
- Click on Code Downloads & Errata.
- Enter the name of the book in the Search box.
- Select the book for which you're looking to download the code files.
- Choose from the drop-down menu where you purchased this book from.
- Click on Code Download.
You can also download the code files by clicking on the Code Files button on the book's webpage at the Packt Publishing website. This page can be accessed by entering the book's name in the Search box. Please note that you need to be logged in to your Packt account.
Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:
- WinRAR / 7-Zip for Windows
- Zipeg / iZip / UnRarX for Mac
- 7-Zip / PeaZip for Linux
The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Practical-Machine-Learning-Cookbook. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/ . Check them out!
We also provide you with a PDF file that has color images of the screenshots/diagrams used in this book. The color images will help you better understand the changes in the output. You can download this file from https://www.packtpub.com/sites/default/files/downloads/PracticalMachineLearningCookbook_ColorImages.pdf.
Although we have taken every care to ensure the accuracy of our content, mistakes do happen. If you find a mistake in one of our books-maybe a mistake in the text or the code-we would be grateful if you could report this to us. By doing so, you can save other readers from frustration and help us improve subsequent versions of this book. If you find any errata, please report them by visiting http://www.packtpub.com/submit-errata, selecting your book, clicking on the Errata Submission Form link, and entering the details of your errata. Once your errata are verified, your submission will be accepted and the errata will be uploaded to our website or added to any list of existing errata under the Errata section of that title.
To view the previously submitted errata, go to https://www.packtpub.com/books/content/support and enter the name of the book in the search field. The required information will appear under the Errata section.
Piracy of copyrighted material on the Internet is an ongoing problem across all media. At Packt, we take the protection of our copyright and licenses very seriously. If you come across any illegal copies of our works in any form on the Internet, please provide us with the location address or website name immediately so that we can pursue a remedy.
Please contact us at [email protected] with a link to the suspected pirated material.
We appreciate your help in protecting our authors and our ability to bring you valuable content.
If you have a problem with any aspect of this book, you can contact us at [email protected], and we will do our best to address the problem.