What are common ML algorithms?

Some typical machine learning (ML) models applied in natural language processing (NLP) are supervised models, such as Naive Bayes, Support Vector Machines (SVM), and Logistic Regression, and deep learning models, such as Recurrent Neural Networks (RNNs) and Transformers.​

Controlled Learning Algorithms.

Naive Bayes: A probabilistic classifier, which makes the independence between features, one that is effective in text classification such as spam is fast and easy to use.​

Support Vector Machines (SVM): Identifies the ideal hyperplanes to distinguish between classes in a high-dimensional text-based data, which can be applied in sentiment analysis and document classification.​

Logistic Regression: The inputs are categorized into category probabilities, which are usually used in NLP to predict binary inputs such as toxicity detection.​

Decision Trees/ random Forests: Construct tree models to perform classification; the Random Forests include many trees to process large text documents and minimize overfitting.​

Deep Learning Algorithms

Recurrent Neural Networks (RNNs): Process sequential data by storing hidden states, and is best suited to problems such as language modeling though difficult with issues such as vanishing gradients.​

Convolutional Neural Networks (CNNs): Learn local text patterns using filters, and are useful at classification and translation.​

Transformers: Attention mechanisms are used to deal with long-range dependencies, which drives the state-of-the-art NLP models such as BERT to comprehend.​

Such algorithms allow the ML-based NLP to acquire patterns based on data, which are better than rule-based approaches to complexity and scale.