Machine learning algorithms are currently applied in multiple scenarios in which unbalanced datasets or overall lack of sufficient training data lead to their suboptimal performance. For example, approaches focusing on disease prediction are often affected because data in the health sector is generally difficult to acquire and disease training examples are limited. Fraud detection in
Artificial Intelligence and Machine Learning has captured a large share of academic and industry attention during recent years, both in terms of new capabilities and the implications to society. Many state-of-the-art techniques are able to provide important capabilities for different fields, yet we are far from creating artificial general intelligence. Human-In-The-Loop (HITL) is a branch
Since Frank Rosenblatt introduced the Perceptron in 1958, neural networks have significantly evolved and taken the world by storm. Their ability to model complex, non-linear relationships that exist in data, led to novel neural network architectures, able to outperform humans in various challenging tasks like face recognition, disease prognosis and playing video games. However, even
Recently, I have finished a project working on testing Machine Learning algorithm performances in different data science platforms with an explicit focus on explainability. In this post, I shall describe some of the criteria and the platforms that were used in this project. In the era of the internet where vast amounts of data are
