Maximum Likelihood Estimate (MLE) is a fundamental method that enables any Machine Learning model to learn distinctive patterns from the available data. In this blog post, we will learn about the concept of Maximum Likelihood Estimates via its application to the next-word prediction problem to make the explanations more intuitive.
Note: This article is going to use some concepts from Probability and Machine Learning theory. I’ll try my best to keep it as general as possible and would provide links to background reading on essential concepts as and when I introduce them.
One of the common features of a good keyboard application is the prediction of upcoming words. These predictions get better and better as you use the application, thus saving users’ effort. Another application for text prediction is in search engines. Predictive search saves effort and guides visitors to results, rather than having them type searches that are slightly off and don’t return a large number of results. As a consumer of these applications, I am sure you would have wondered “How exactly does this…