Examples of use of Probabilistic Machine Learning (for longitudinal data) Reserving with scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce
Recruiting the right talent has always been a critical challenge for businesses. With competition for skilled professionals growing fiercer, companies can no longer afford slow, inefficient hiring processes. This is where recruitment software, such as JobAdder’s RMS software, can be a game-changing tool that saves time and significantly boosts ...
Well, dear reader, I know I haven’t been posting very much lately. That’s because I’ve been busy moving to a new city and working a new DS gig and learning some new things, including Bayesian modeling. In particular I’ve been reading Richard McEl...
Bokeh is another data visualization library available in Python. One of Bokeh’s unique features is that it allows for interaction. In this post, we will learn how to make a basic scatterplot in Bokeh while also exploring some of the basic interactions that are provided by default. Data Preparation ...
A flexible hybrid approach to probabilistic stock forecasting that combines machine learning with ARCH effects, offering an alternative to traditional ARMA-GARCH models
I’ve been working with Docker for years now across several development teams, and I’ve got to say—there’s a world of difference between knowing basic Docker commands and actually integrating Docker deeply into your development workflow. After countless hours of troubleshooting environment issues and hearing “but it ...
As an Excel trainer and course creator who often covers more advanced topics, I get a lot of questions about these newer features, particularly Python in Excel and Copilot in Excel. “When should I use these?” “How do I actually use them well?” “Does this mean regular Excel isn’t ... [...Read more...]
