Machine Learning Articles

Image recognition uses algorithms and models to interpret the visual world, converting images into symbolic information for use in various applications.

Find out everything you need to know about machine learning in 2023, including its types, uses, careers, and how to get started in the industry.

Find out how you can master AI and benchmark your skills with DataCamp. Learn about LLMs, Generative AI, AI Ethics, and how to implement ChatGPT and other AI solutions in business.

Algorithmic bias results in unfair outcomes due to skewed or limited input data, unfair algorithms, or exclusionary practices during AI development.

Synthetic data is artificially generated data that mimics the characteristics of real-world data without containing any actual information.

In the machine learning universe, unlabeled data is primarily used in unsupervised learning models.

Labeled data is raw data that has been assigned labels to add context or meaning, which is used to train machine learning models in supervised learning.

Incremental learning is a methodology of machine learning where an AI model learns new information over time, maintaining and building upon previous knowledge.

Affective computing is a field that strives to create more personalized and intuitive interactions between humans and machines.

Narrow AI refers to artificial intelligence systems that are designed to perform specific tasks and operate under limited constraints.

Text generation is a process where AI produces text that resembles natural human communication.

Lazy learning algorithms work by memorizing the training data rather than constructing a general model.