In machine learning, a Bayes classifier is a simple probabilistic classifier, which is based on applying Bayes’ theorem. The feature model used by a naive Bayes classifier makes strong independence assumptions. This means that the existence of a particular feature of a class is independent or unrelated to the existence of every other feature. Naive Bayes simplifies the calculation of probabilities by assuming that the probability of each attribute belonging to a given class value is independent of all other attributes. This is a strong assumption but results in a fast and effective method. The probability of a class value given a value of an attribute is called the conditional probability. By multiplying the conditional probabilities together for each attribute for a given class value, we have a probability of a data instance belonging to that class. To make a prediction we can calculate probabilities of the instance belonging to each class and select the class value with the highest probability.
In early 2015, people started using messaging applications more than they use social networks. This is a significant shift and a huge turning point in how consumers consume information. Up until 2015, to market a business online, we would use social networks – as this is where the consumers were. Now, there is a better place to concentrate resources. Businesses that seize opportunity are the ones that follow consumers the fastest. Think back to 5 or so year ago. “There’s an app for that” – said, everyone. Now it is probably too late for a business to create an app, similar functionality can probably be better delivered elsewhere. I certainly do not think any sane person would form an app-building start-up. It is not just consumer trends. Another contributing factor is the commercial opportunity, and therefore, interest from large (wealthy) companies. The platforms that enable the delivery of chatbot experiences are opening up to larger audiences and more innovative ways of creating an ROI and user interaction are being rapidly developed. It is the culmination of the consumer behavior (moving to message apps) and the technology is ready, along with a greater cultural shift in consumer behavior. People have been using messaging apps (and SMS) to talk with friends and family for long enough to feel confident in using the same practices to communicate with a business. This coincides with businesses now having the tools and technology to effectively communicate through the apps in a way consumers require. The near-future potential is quite apparent. No longer will consumers have to trawl through websites and search engines to find the information they need. Instead, they will be communicating with intelligent chatbots at every stage.
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Machine Learning has been defined as “the future” and is being deployed in many organizations to achieve real business results. With its dramatic improvements in past few years, Machine Learning is expected to go far beyond the highest level of accuracy and understanding.