1. Word2Vec: Word2Vec is a shallow neural network model used to generate word embeddings. It takes in a large corpus of text and produces a vector space, typically of several hundred dimensions, with each unique word in the corpus being assigned a corresponding vector in the space.
2. Latent Dirichlet Allocation (LDA): LDA is a generative probabilistic model used to discover the underlying topics in a corpus of documents. It takes in a set of documents and produces a set of topics, each of which is associated with a set of words.
3. Hidden Markov Models (HMMs): HMMs are used to model sequences of observations, such as words in a sentence or audio signals. They use a set of hidden states to model the probability of observing a particular sequence of observations.
4. Naive Bayes: Naive Bayes is a probabilistic classifier used to predict the class of a given input. It uses Bayes’ theorem to calculate the probability of an event occurring based on the evidence provided by a set of features.
5. Support Vector Machines (SVMs): SVMs are a type of supervised learning algorithm used for classification and regression. They use a set of hyperplanes to separate data points into different classes.