Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.

Feature engineering is the process of selecting, manipulating and transforming raw data into features that can be used in supervised learning.

Clustering algorithms are procedures for partitioning data into groups or clusters such that the clusters are distinct, and members of each cluster belong together.

Shallow learning algorithms are machine learning models that typically have one or two layers of processing.

Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning.

LLM stands for Large Language Model, which is a type of artificial intelligence (AI) that uses machine learning to generate and understand human language.

Artificial intelligence (AI) is a set of technologies that enable computers to perform a variety of advanced functions, including the ability to see, understand and translate spoken and written language, analyze data, make recommendations, and more.