Data Science Applications
Supervised Learning
Supervised learning involves training a model on a labeled dataset, which means that each training example is paired with an output label. The goal is for the model to learn a mapping from inputs to outputs so it can predict the output for new, unseen inputs.
Unsupervised Learning
Unsupervised learning involves training a model on data without labeled responses. The goal is to find hidden patterns or intrinsic structures in the input data.
Classification
Classification is a type of supervised learning where the goal is to predict a discrete class label for a given input.
Regression
Regression is a type of supervised learning where the goal is to predict a continuous value for a given input.
Computer Vision
Computer vision is a field of artificial intelligence that enables computers to interpret and make decisions based on visual data from the world.
Natural Language Processing (NLP)
NLP involves the interaction between computers and humans through natural language. The goal is to enable computers to understand, interpret, and generate human language.