Название: Practical Machine Learning in R Автор: Kyriakos Chatzidimitriou, Themistoklis Diamantopoulos
Издательство: Leanpub
Год: 2018
Страниц: 148
Язык: английский
Формат: pdf (true), djvu
Размер: 10.1 MB
Do you want to start using R for crunching machine learning models right from the start with examples? Then this book is for you. The book is about quickly entering the world of creating machine learning models in R. The theory is kept to minimum and there are examples for each of the major algorithms for classification, clustering, features engineering and association rules.
R is an open source programming language and a free environment, mainly used for statistical computing and graphics. Even through R comes with its own environment: command line and graphical interfaces, one can use the popular RStudio, which offers additional graphical functionalities.
Machine Learning (ML) is a subset of Artificial Intelligence (AI) in the field of computer science that often uses statistical techniques to give computers the ability to “learn” (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed. Machine Learning is often closelly related, if not used as an alternate term, to fields like Data Mining (the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems), Pattern Recognition, Statistical Inference or Statistical Learning. All these areas often employ the same methods and perhaps the name changes based on the practitioner’s expertise or the application domain.
The main ML tasks are typically classified into two broad categories, depending on whether there is “feedback” or a “teacher” available to the learning system or not:
- Supervised Learning: The system is presented with example inputs and their desired outputs provided by the “teacher” and the goal of the machine learning algorithm is to create a mapping from the inputs to the outputs. The mapping can be thought of as a function that if it is given as an input one of the training samples it should output the desired value.
- Unsupervised Learning: In the unsupervised learning case, the machine learning algorithm is not given any examples of desired output, and is left on its own to find structure in its input.
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