Source: Proofreader’s Whimsy
What do you do when your dataset doesn’t have any labels? Unsupervised learning is a group of Machine Learning algorithms and approaches that work with this kind of “no-ground-truth” data. This post will walk through what unsupervised learning is, how it’s different than most Machine Learning, some challenges with implementation, and resources for further reading.
Using software to parse the world’s visual content is as big of a revolution in computing as mobile was 10 years ago, and will provide a major edge for developers and businesses to build amazing products.
Computer Vision is the process of using machines to understand and analyze imagery (both photos and videos). While these types of algorithms have been around in various forms since the 1960’s, recent advances in Machine Learning, as well as leaps forward in data storage, computing capabilities, and cheap high-quality input devices, have driven major improvements in how well our software can explore this kind of content. Read More…
One of the most compelling use cases of sentiment analysis today is brand awareness. If you can understand what people are saying about you in a natural context, you can work towards addressing key problems and improving your business processes. So how exactly can you get that up and running?
The Algorithmia marketplace makes it easy to extract the content you need from Twitter and pipe it into the right algorithms for sentiment analysis. There are a few algorithms on the platform for exploring different information from Twitter (like users, tweets, and followers), and a number for sentiment analysis.
Machine Learning is about making predictions. This post will give an introduction to Machine Learning through a problem that most businesses face: predicting customer churn.
ML can help predict which of your customers are at risk for leaving in advance, and give you an edge by pre-empting with action.
Source: Frontiers in Psychology
You expect employees to have high levels of emotional intelligence when interacting with customers. Now, thanks to advances in Deep Learning, you’ll soon expect your software to do the same.
Research has shown that over 90% of our communication can be non-verbal, but technology has struggled to keep up, and traditional code is generally bad at understanding our intonations and intentions. But emotion recognition – also called Affective Computing – is becoming accessible to more types of developers. This post will walk through the ins-and-outs of determining emotion from data, and a few ways you can get some emotion recognition and running yourself.