There are five main types of machine learning, each offering distinct ways for models to learn based on data structure and environmental interaction:
1) Supervised learning: In this approach, models are trained using labeled data, where the correct output is known. “Supervised” indicates that these labels guide the model in learning relationships between inputs and outputs, enabling tasks like classification and prediction. Learn more about supervised learning.
2) Unsupervised learning: In contrast to supervised learning, unsupervised learning works with unlabeled data to identify patterns or groupings in the data without being told the correct answers. This method is used for tasks like clustering and dimensionality reduction. Learn more about unsupervised learning.
3) Semi-supervised learning: This method combines both labeled and unlabeled data, using a small portion of labeled data to guide the learning process, which is then applied to a larger unlabeled dataset. Learn more about semi-supervised learning.
4) Reinforcement learning: Models in reinforcement learning interact with an environment and learn through trial and error, aiming to maximize rewards over time. Learn more about reinforcement learning.
5) Self-supervised learning: In this recent machine learning development, self-supervised learning uses unlabeled data to generate labels from the data itself. This approach is seen in large language models like GPT. Learn more about self-supervised learning.