Machine Learning | Language Models | Generative AI

The field of machine learning is generally considered to have originated in the 1950s, although the mathematical methods and algorithms it is built upon date much earlier. Arthur Samuel, a prominent American computer scientist, defined machine learning as "the field of study that gives computers the ability to learn without being explicitly programmed".
There are three different machine learning tasks that can be carried out on structured data - regression involves predicting a continuous, numerical value such as a house price, classification entails predicting a discrete class or category such as a dog breed and clustering is concerned with finding natural groups in unlabelled data such as customer segmentation.
The table shown is a small extract from one of the thousands of benchmark datasets widely used in education and research to learn and experiment with different models. This particular dataset lends itself to classification, in this case predicting if a passenger on the Titanic was likely to survive the disaster using a number of numerical and categorical feature values.

As machine learning has developed it has been influenced by the software development lifecycle. A machine learning workflow can be thought of as structured sequence of steps.

Explore best practices for data preprocessing, model selection, and deployment strategies to ensure reliable AI systems.

Text embedding or to simplify greatly, the process of converting user text into meaningful numerical representation, is perhaps the one element of how Large Language Models that is many are leat able to understand.

With AI Modeler you will use models built using deep learning architecture.