MLDB Procedures are used to train models, which can then be applied via Functions.

- The
`classifier.train`

procedure type can train: - The
`svm.train`

procedure type can train Support Vector Machines (SVM) - The
`probabilizer.train`

procedure type can calibrate classifiers

- The
`tensorflow.graph`

function type can execute TensorFlow models

- The
`kmeans.train`

procedure type can train K-Means models

- The
`svd.train`

procedure type can perform Truncated Singular Value Decompositions (SVD) - The
`tsne.train`

procedure type can perform t-distributed Stochastic Neighbor Embedding (t-SNE)

- The
`import.sentiwordnet`

procedure type can import SentiWordNet models - The
`import.word2vec`

procedure type can import Word2Vec embeddings - The
`tfidf.train`

procedure type can train Term-Frequency/Inverse-Document-Frequency (TF-IDF) models - The
`statsTable.train`

procedure type can assemble tables of counts to assemble count-based features - The
`feature_hasher`

function type can be used to do feature hashing, which is a way to vectorize features