Category Archives: Machine Learning

I was talking with a colleague about naive Bayes classification. Naive Bayes was originally designed to work with categorical data, such as height values that are short, medium, or tall. I mentioned that when I use naive Bayes on continuous … Continue reading →

I wrote an article titled “How To Do Logistic Regression Using ML.NET” in the October 2019 issue of Visual Studio Magazine. See https://visualstudiomagazine.com/articles/2019/10/17/logistic-regression-mlnet.aspx. ML.NET is a code library (or a framework, depending on your point of view) that allows developers … Continue reading →

When working with neural networks, categorical data must be encoded. For example, suppose one of your predictor variables is region, which can take a value of “eastern”, “western”, or “central”. The two most common approaches for encoding this kind of … Continue reading →

Linear regression is a technique that can be used to predict a single numeric value. For example, you might want to predict the annual income of a person based on their sex, age, job type, and job satisfaction. The form … Continue reading →

A perceptron is a simple software system that can make binary predictions, such as whether a person is male or not, based on predictor variables like age, annual income, and so on. Perceptrons are among the simples forms of machine … Continue reading →

I wrote an article titled “Getting Started with AutoML for ML.NET” in the September 2019 issue of Visual Studio Magazine. See https://visualstudiomagazine.com/articles/2019/09/30/automl-mlnet.aspx. Microsoft ML.NET is a code library for .NET Core (the multi-platform version of the .NET Framework). The ML.NET … Continue reading →

Deep neural networks have more or less taken over the mindshare of machine learning. There are many ML techniques that are slowly being forgotten. Three of these increasingly seldom-used techniques are radial basis function networks, self-organizing maps, and restricted Boltzmann … Continue reading →

Last week, while I was walking my dogs, I was thinking about logistic regression (LR). LR is a technique to perform binary classification, such as predicting if a person is male or female based on their age, job type (mgmt, … Continue reading →

There are many neural network libraries. TensorFlow, Keras and PyTorch are among the best known but there are dozens of others. Most of the neural network code libraries seem to be evolving towards common techniques and interfaces. For example, the … Continue reading →

I’ve been involved with machine learning for many years. Now that ML and AI are increasingly important, developers often ask me some form of the question, “What are the fundamental ML techniques I should know?” I’ve given this a lot … Continue reading →