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people have been exposed to the term and are looking at how to leverage big data in their
own businesses, to improve sales and profitability. However, collecting, aggregating, and
visualizing data is just one part of the equation. Being able to extract useful information from
data is another task, and much more challenging.
Traditionally, most researchers perform statistical analysis using historical samples of
data. The main downside of this process is that conclusions drawn from statistical analysis
are limited. In fact, researchers usually struggle to uncover hidden patterns and unknown
correlations from target data. Aside from applying statistical analysis, machine learning has
emerged as an alternative. This process yields a more accurate predictive model with the
data inserted into a learning algorithm. Through machine learning, the analysis of business
operations and processes is not limited to human-scale thinking. Machine-scale analysis
enables businesses to discover hidden values in big data.
The most widely used tool for machine learning and data analysis is the R language. In
addition to being the most popular language used by data scientists, R is open source and is
free for use for all users. The R programming language offers a variety of learning packages
and visualization functions, which enable users to analyze data on the fly. Any user can
easily perform machine learning with R on their dataset without knowing every detail of the
mathematical models behind the analysis.
Machine Learning with R Cookbook takes a practical approach to teaching you how to perform
machine learning with R. Each of the 12 chapters are introduced to you by dividing this topic
into several simple recipes. Through the step-by-step instructions provided in each recipe, the
reader can construct a predictive model by using a variety of machine learning packages.
machine-learning-with-r-cookbook.pdf
(6.01 MB, 需要: 10 个论坛币)
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