Jeff Prosise

The three previous posts in this series introduced binary classification and provided working examples of its use, including sentiment analysis and spam filtering. Now it’s time to tackle multiclass classification, in which there are n possible outcomes rather than just...

My previous post introduced a machine-learning model that used logistic regression to predict whether text input to it expresses positive or negative sentiment. We used the probability that the text expresses positive sentiment as a sentiment score, and saw that...

One of the more novel yet practical uses for binary classification is sentiment analysis, which examines a piece of text such as a product review, a tweet, or a comment left on a Web site and scores it on a...

The machine-learning model featured in my previous post was a regression model that predicted taxi fares based on distance traveled, the day of the week, and the time of day. Now it’s time to tackle classification models, which predict categorical...

When you build a machine-learning model, the first and most important decision you make is what learning algorithm to use to fit the model to the training data. In my previous post, I introduced some of the most widely used...

Supervised-learning models come in two varieties: regression models and classification models. Regression models predict numeric outcomes, such as the price of a car. Classification models predict classes, such as the breed of a dog in a photo. When you build...

Most machine-learning models fall into one of two categories. Supervised-learning models make predictions. For example, they predict whether a credit-card transaction is fraudulent or a flight will arrive on time. Unsupervised-learning models don’t make predictions; they provide insights into existing...

Machine-learning models fall into two broad categories: supervised-learning models and unsupervised-learning models. The purpose of supervised learning is to make predictions. The purpose of unsupervised learning is to glean insights from existing data. One example of unsupervised learning is examining...

Machine-learning models fall into two broad categories: supervised-learning models and unsupervised-learning models. The purpose of supervised learning is to make predictions. The purpose of unsupervised learning is to glean insights from existing data. One example of unsupervised learning is examining...

Machine learning (ML) and artificial intelligence (AI) are transforming the way software is written and, more importantly, what software is capable of. Developers are accustomed to writing code that solves problems algorithmically. It’s not difficult to write an app that...

Deep learning is a subset of machine learning that relies on deep neural networks. It is how computers identify objects in images, translate speech in real-time, generate artwork and music, and perform other tasks that would have been impossible just a few short years ago

Need to incorporate machine learning (ML) and AI into your next project, but not excited about learning Python? ML.NET is Microsoft's open-source ML library for .