Jeff Prosise

One of the common uses for machine learning is performing binary classification, which looks at an input and predicts which of two possible classes it belongs to. Practical uses include sentiment analysis, spam detection, and credit-card fraud detection. Such models...

Machine learning isn’t hard when you have a properly engineered dataset to work with. The reason it’s not hard is libraries such as Scikit-learn and ML.NET, which reduce complex mathematical manipulations to a few lines of code. Deep learning isn’t...

Need to write an app that uses facial recognition to identify people entering a building?

Deep learning is a subset of machine learning that relies primarily on neural networks. Most of what’s considered AI today is accomplished with deep learning. From recognizing objects in photos to real-time speech translation to using computers to generate art,...

Scikit-learn is arguably the world’s most popular machine-learning framework. The efficacy of the library, the documentation that accompanies it, and the mindshare that surrounds it are the primary reasons more ML models are written in Python than any other language.

All of the machine-learning models presented so far in this series were written in Python. Models don’t have to be written in Python, but many are, thanks in part to the numerous world-class Python libraries that are available, including Pandas and...

Another branch of machine learning that has proven its mettle in recent years is recommender systems – systems that recommend products or services to customers. Amazon's recommender system reportedly drives 35% of its sales. The good news is that you...

Anomaly detection is a branch of machine learning that seeks to identify anomalies in datasets or data streams. Airbus uses it to predict failures in jet engines and detect anomalies in telemetry data beamed down from the International Space Station....

Principal Component Analysis, or PCA, is one of the minor miracles of machine learning. It’s a dimensionality-reduction technique that reduces the number of dimensions in a dataset without sacrificing a commensurate amount of information. While that might seem underwhelming on...

Support-vector machines, also known as SVMs, represent the cutting edge of statistical machine learning. They are typically used for classification problems, although they can be used for regression, too. SVMs often succeed at finding separation between classes when other models...

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...