It is widely used in many industries, businesses, educational and medical research fields. This field has evolved significantly over the past few years, from basic statistics and computational theory to the advanced region of neural networks and deep learning. Explaining how a specific ML model works can be challenging when the model is complex. In some vertical industries, data scientists must use simple machine learning models because it’s important for the business to explain how every decision was made. That’s especially true in industries that have heavy compliance burdens, such as banking and insurance.
Virtual assistants, like Siri, Alexa, Google Now, all make use of machine learning to automatically process and answer voice requests. They quickly scan information, remember related queries, learn from previous interactions, and send commands to other apps, so they can collect information and deliver the most effective answer. In this example, a sentiment analysis model tags a frustrating customer support experience as “Negative”. Machine learning applies to a considerable number of industries, most of which play active roles in our daily lives. Just to give an example of how everpresent ML really is, think about speech recognition, self-driving cars, and automatic translation.
