A support vector machine is a linear machine learning model for classification and regression problems. Learn how it works and how to implement it in Python.
Model deployment is the process of integrating a machine learning model into a production environment where it can take in an input and return an output.
Natural language processing (NLP) is a branch of artificial intelligence that provides a framework for computers to understand and interpret human language.
Reinforcement learning relies on an agent learning to determine accurate solutions from its own actions and the results produced in a contained environment.
Machine learning algorithms fuel machine learning models. They consist of three parts: a decision process, an error function and a model optimization process.
Feature importance involves calculating a score for all input features in a machine learning model to determine which ones are the most important. Here’s how to do it.
Non-maximum suppression (NMS) is a post-processing technique that is used in object detection tasks to eliminate duplicate detections and select bounding boxes.
