Machine learning has grown in popularity with websites like StitchFix and Netflix learning human behaviors and replicating them to put forth ‘custom’ recommendations. As we learn in a video interview with Director of Data Science, Brian Beesley and EVP of Strategic Partnerships, Woody Walker, there are many use cases for machine learning across retail, manufacturing, and financial service industries.
Machine Learning Defined
To put it simply, machine learning is the study of algorithms that computers use to perform a specific task without explicit instructions. The larger the data sets, the more patterns that the machine can detect.
More Data = More Learnings
Machine learning isn’t new. Even in the 1980s computers have learned patterns and recommended outputs. The big difference in today’s analytics landscape isn’t the quality of math or even functionality, but the sheer volume of data. Once a machine has more data to learn from, that opens to even more use cases. Another critical enhancement overtime is the improvement of hardware, like graphical processing units and data storage, resonating with the evolution of the cloud.
Flipping the Process
Commonly used within the analytics process is a term called ETL, or Extract Transform Load. This describes the process of moving data from a source to its destination system, which can represent the data differently or in varying contexts. As modern data storage systems have grown and machine learning has advanced, it is more efficient to flip the process to ELT, loading the data before transforming. Data Scientists often work with the business members in the transformation stage to see what data is missing or what process improvements are needed. In ELT, you take an exploratory analysis then formalize it later, learning every step of the way.
Speech recognition is one of the most commonly referred-to source of machine learning which uses a process called supervised learning. Within supervised learning, the computer system learns a function that maps an input to an output based on the ‘answers’ that you feed. Apple’s Siri or Amazon’s Alexa are two common examples of speech recognition. Optical Character Recognition is another source of machine learning, which converts different mediums like handwritten or printed text into editable or searchable data.
Machine learning can be applied to a variety of industries and below are some common examples.
The value and return on investment from a machine learning implementation come from the identification of a use case for your business and the organization’s willingness to adopt the solution. At the end of the day, if your company could improve with instantaneous and accurate decision making, then it’s an investment worth making.
Learn more about implementing machine learning in your business by connecting with one of our Data Science Experts here: https://go.ccganalytics.com/data-science-solutions-services-offer.
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