In this project, we will be using scikit-learn pipelines to train our random forest algorithm and build a drug classifier. After training, we will automate the evaluation process using CML. Finally, ...

For a supervised machine learning project, you will need to label the data in a meaningful way for the outcome you are seeking. In Table 1, note that each row of the house record includes a label ...

Quantum machine learning (QML) is transitioning from research to practical business applications. Discover how QML is ...

This article looks at 13 open source projects that are remaking the world of AI and machine learning. Some are elaborate software packages that support new algorithms. Others are more subtly ...

Data cleaning and Exploratory Data Analysis (EDA) might not seem glamorous, but the process is vital for guiding your real-world data projects. The chances are that you have heard of linear regression ...

In this paper, we present an overview of a project-based course on MLOps by showcasing a couple of sample projects developed by our students. Additionally, we s. Training Future Machine Learning ...

Examine how to build explainability in a machine learning project; Find out more. Customised solutions. ... Related training. AI integration into operational risk management. Date. November 4–6, 2025 ...

But as machine learning models grow in number and size, they will require more training data. Unfortunately, obtaining training data and annotating still requires considerable manual effort and is ...