Today’s developers often hear about leveraging machine learning algorithms in order to build more intelligent applications, but many don’t know where to start.
One of the most important aspects of developing smart applications is to understand the underlying ML models, even if you aren’t the person building them. Whether you are integrating a recommendation system into your app or building a chat bot, this guide will help you get started in understanding the basics of machine learning.
What is Machine Learning?
Computational learning using algorithms to learn from and make predictions on data.
While this is only a brief definition, ML means we can use statistical models and probabilistic algorithms to answer questions so we can make informative decisions based on our data.
An excerpt from Rob Schapire’s Theoretical Machine Learning lecture in 2008 sums it up very nicely:
Machine learning studies computer algorithms for learning to do stuff. We might, for instance, be interested in learning to complete a task, or to make accurate predictions, or to behave intelligently. The learning that is being done is always based on some sort of observations or data, such as examples…direct experience, or instruction. So in general, machine learning is about learning to do better in the future based on what was experienced in the past.
This article originally appeared in blog.algorithmia.com. To read the full article, click here.