In part 1 of this introductory series on uncertainty in ML models, we introduce several proven concepts and methods for identifying two types of uncertainty and evaluating them with statistical methodologies.
XGBoost's ability to deliver state-of-the-art performance with efficient training and a rich set of features has made it a go-to choice for Machine Learning practitioners. In this overview we will see what makes the algorithm so powerful
In this article, we examine the use of synthetic data for various Data Science tasks, and consider the advantages and limitations of using it for real world ML tasks.
In this tutorial, we show how to use classical ML techniques to optimize theoretical energy supply chains.
This is a review of the CausalML package, a Python package that provides a suite of uplift modeling and causal inference methods using machine learning algorithms based on recent research.
In this article, we explain the SVM algorithm generally, and then show how to use the LIBSVM package in a code demo. After the code section, we will share some additional tips to help improve the performance of our model, as well as some assumptions and limitations of the algorithm.
In this blog post, we cover the three types of recommender systems, and demo their use with the MovieLens dataset.
In this blog post we examine the growing technology of weakly supervised learning, in the context of other machine/deep learning techniques, and discuss some of the potential applications and frameworks that make use of them.
We spoke with Alex Castrounis from the 1871 innovation hub in Chicago to learn more about the intersection of academic and enterprise-focused machine learning instruction.
