Machine Learning: 2 Books in 1: Machine Learning for Beginners, Machine Learning Mathematics. An Introduction Guide to Understand Data Science Through the Business Application

 
Data Mining
Data mining can be defined as “the process of exploring and analyzing large
volumes of data to gather meaningful patterns and rules”. Data mining falls
under the umbrella of data science and is heavily used to build artificial
intelligence-based machine learning models, for example, search engine
algorithms. Although the process of “digging through data” to uncover
hidden patterns and predict future events has been around for a long time
and referred to as “knowledge discovery in databases”, the term “Data
mining” was coined as recently as the 1990s.

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According to SAS, “unstructured data alone makes up 90% of the digital
universe”. This avalanche of big data would not essentially guarantee more
knowledge. The application of data mining technology allows filtering of
all the redundant and unnecessary data noise to garner the understanding of
relevant information that can be used in the immediate decision-making
process.
Data mining consists of three foundational and highly intertwined
disciplines of science, namely, “statistics” (the mathematical study of data
relationships), “machine learning algorithms” (algorithms that can be
trained with an inherent capability to learn) and “artificial intelligence”
(machines that can display human-like intelligence). With the advent of the
big data, Data mining technology has been evolved to keep up with the
“limitless potential of big data” and relatively cheaper advanced computing
abilities. The once considered tedious, labor-intensive, and time-consuming
activities have been automated using advance processing speed and power
of the modern computing systems.
Data Mining Trends
Increased Computing Speed
With increasing volume and complexity of big data, Data mining tools need
more powerful and faster computers to efficiently analyze data. The existing
statistical techniques like "clustering" art equipment to process only
thousands of input data with a limited number of variables. However,
companies are gathering over millions of new data observations with

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hundreds of variables making the analysis too complicated for the
computing system to process. The big data is going to continue to explode,
demanding supercomputers that are powerful enough to rapidly and
efficiently analyze the growing big data.