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.
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.
Supervised learning is an approach to machine learning that uses labeled data sets to train algorithms in order to properly classify data and predict outcomes.
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.
Python is a popular programming language to use in machine learning because it offers developers exceptional versatility and power while integrating with other software.
Non-maximum suppression (NMS) is a post-processing technique that is used in object detection tasks to eliminate duplicate detections and select bounding boxes.
