ML stands for Machine Learning. Wikipedia quotes Arthur Samuel who defined machine learning as a “Field of study that gives computers the ability to learn without being explicitly programmed.” In common words, Machine Learning provides software tools and framework to detect trends and predict future events. Machine Learning and Data Science are used somewhat interchangeably and have gained prominence in the recent years. The value this field provides is invaluable for companies to differentiate themselves from competitors and helps them serve their customers better. The trick, however, is to understand what kinds of machine learning tools are best suited to the particular business objective.
A Machine Learning model’s success, as you can imagine, is tightly coupled with the quality of data available. So gathering and preparation of data is a critical step in the overall process of creating ML models. Even though a large number of companies now have an easy to use modeling framework, what they overlook is the data cleansing aspect.
We at springML provide end to end services in the field of Analytics including data gathering and exploration, model building and evaluation, and model output consumption and visualization.