Windows 11 — Initial Reactions and Thoughts
Windows 11 made a big splash with the announcement of the new version of the OS that was supposed to never have another version again. In any case, as part […]
Multiclass Classification
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, […]
Binary Classification: Spam Filtering
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 […]
Binary Classification: Sentiment Analysis
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 […]
Binary Classification
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. […]
App Services Migration Assistant Assessment Report
Azure App Services is a popular choice for migrating apps to Azure because it gets users out of the business of managing servers. But with App Services, some limitations might […]
Regression Modeling
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 […]
Starting an Assessment with Discovery
The first critical step in migrating anything to Azure is an assessment of what we want to move. The assessment starts with a discovery wherein various aspects of a workload are teased out to expose what composes and supports the workload.
Supervised Learning with k-Nearest Neighbors
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. […]