Reinforcement learning is a method of training machine learning models through trial and error and feedback. The model will be given a goal and list of known

Machine learning algorithms learn to make predictions or decisions by learning, from historical data, a model of the underlying process connecting inputs (a.k.a. features) and

Decision trees in machine learning are a common way of representing the decision-making process through a branching, tree-like structure. It’s often used to plan and

Machine Learning Regression is a technique for investigating the relationship between independent variables or features and a dependent variable or outcome. It’s used as a

The term “MLOps” is a compound of Machine Learning and Operations. It refers to the practice of deploying, managing and monitoring machine learning models in

Covariate shift is a specific type of dataset shift often encountered in machine learning. It is when the distribution of input data shifts between the training

Cross validation is the use of various techniques to evaluate a machine learning model’s ability to generalize when processing new and unseen datasets. Generalization is

The concept of optimization is integral to machine learning. Most machine learning models use training data to learn the relationship between input and output data.

Deep learning is a form of machine learning which aims to mimic and imitate the processes of the human brain through machine learning algorithms. The resulting

Machine learning deployment is the process of deploying a machine learning model in a live environment. The model can be deployed across a range of