We at OXO Solutions provide services in Computer Vision, Natural Language Processing, Predictive Analytics, and much more. Your idea and our hands at work can lead us both in the right direction.
From face recognition models to Text Analytics, we have everything set up, your customization and our optimization is what will make a successful Machine Learning Model for your business.
What is Machine Learning ?
Machine learning is a technique of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the concept that systems can recognize patterns, learn from data and make decisions with the slightest human intervention.
Machine learning has regained popularity due to the similar factors that have made data mining and Bayesian analysis more popular than ever. There are many things that are brought into consideration including growing volumes of data, availability of cheap and powerful computational processing, and inexpensive data storage. All these factors imply that it is feasible to speedily and automatically produce models that can analyze large, more complex and deliver faster and more accurate results even on a large scale. By building an accurate model, an organization has a better opportunity to identify profits or avoid unknown risks.
Requirements to design a good machine learning system:
Algorithms – basic and advanced
Data preparation capabilities
Automation and iterative processes
Examples of Machine Learning:
Image recognition is a common deep learning case. It is a part of the broader field of computer vision. Through the technique of computer vision, computers are able to comprehend images or other visual media at a high level and automate the tasks that the human eye can carry out. The process of computer vision is enhanced by deep learning. It can be used in self-driving vehicles, which can categorize and detect vehicles and people.
Another crucial example is the application in medicine. Once the images of the blood-soaked sponge are shown the computer can accurately detect blood loss during surgeries in real-time. This method is capable of saving millions of dollars spent in blood transfusions.
Deep learning has made chatbots converse with customers more productively and assist them to solve problems they may face. In the competitive world customer expectations are quite high, thus it is expected from them that their issues are resolved immediately and expect customer care teams to be available round the clock. These expectations are met as intelligent chatbots are powered by deep learning networks. However, there is still work to be done for a chatbot to pass the Turing test, where it can converse in a way that is indistinguishable from that of a human.
Many popular web and mobile applications are operated by engines that implement deep learning to intelligently suggest content and products to customers. Examples of these platforms include Amazon, Netflix, and YouTube. The incredible thing about the deep learning execution is that the wealth of user-generated data assists to repeatedly hone the deep networks that power the recommendation engines. The ultimate outcome is that the users can see products and content most relevant to their specific needs. There are also some ethical implications of amplifying the power of these engines in terms of handling addictive behavior. One example is of ”Netflix binge” which drives people to keep watching their favorite shows because of its intelligent recommendation. Nonetheless, one cannot overlook the positive impact of deep learning that helps people by showing them the most relevant content.