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Machine learning has grown in popularity with websites like StitchFix and Netflix learning human behaviors and replicating them to put forth \u2018custom\u2019 recommendations. There\u00a0are many use cases for machine learning across retail, manufacturing, and financial service industries.<\/p>\n
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Machine Learning Defined<\/strong><\/p>\n 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.<\/p>\n More Data = More Learnings <\/strong><\/p>\n Machine learning isn\u2019t new. Even in the 1980s computers have learned patterns and recommended outputs. The big difference in today\u2019s analytics landscape isn\u2019t 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.<\/p>\n Flipping the Process <\/strong><\/p>\n Commonly used within the analytics process is a term called ETL, or Extract Transform Load<\/strong>. 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.<\/p>\n Speech recognition<\/strong> is one of the most commonly referred-to source of machine learning which uses a process called supervised learning. Within supervised learning,<\/strong> the computer system learns a function that maps an input to an output based on the \u2018answers\u2019 that you feed. Apple\u2019s Siri or Amazon\u2019s Alexa are two common examples of speech recognition. Optical Character Recognition <\/strong>is another source of machine learning, which converts different mediums like handwritten or printed text into editable or searchable data.<\/p>\n Machine learning can be applied to a variety of industries and below are some common examples.<\/p>\n Machine learning in Retail <\/strong><\/a><\/span><\/p>\n Machine learning in Manufacturing <\/strong><\/a><\/span><\/p>\nIndustry Use Cases <\/strong><\/h3>\n
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