Machine Learning, thinking systems, expert systems, knowledge engineering, decision systems, neural networks – all synonymous loosely woven words in the evolving fabric of Artificial Intelligence. Of these Machine Learning (ML) and Artificial Intelligence (AI) are often debated and used interchangeably. broadly speaking AI can be termed as a futuristic state of self aware smart learning machines in true sense, but for all practical purposes we deal more often with ML at present.
In very abstract terms, ML is a structured approach for deriving meaningful predictions/insights from both structured and unstructured data. ML methods employ complex algorithms that enable analytics based on data, history and patterns. The field of data science continues to scale new heights enabled by the exponential growth in computing power over the last decade. Data scientists are continuously exploring new models & methods each day and sometimes it’s scary to even keep pace with the trends. However to keep matters simple, here is a clean starting point.
Below is an attempt to put a simplified visual representation of the popular ML methods leveraged in the data science field along with their classification. Each of these algorithms are encoded through languages such as R, Python, Scala etc to provide a framework to data scientists in solving complex data driven business problems. However there is an underlying maze of statistical and probabilistic abyss that data scientists need to navigate in order to put these methods to meaningful use.
A brief summary of the above ML methods and how they model are presented in the slides below.
Some of the business applications of these ML methods can be classified as shown in below visual.
As data becomes the new oil that drives virtual machines, I conclude with the below quote,
“Without data you’re just a person with an opinion.” – W. Edwards Deming
