> For the complete documentation index, see [llms.txt](https://docs.chaingpt.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.chaingpt.org/overview/learn-the-concepts/machine-learning-ml.md). # Machine Learning (ML) *Machine learning is the field of knowledge upon which modern artificial intelligence systems are built.* As the name might have hinted, machine learning is, at its core, the construction of methods in which computers can recursively educate and advance their comprehension of certain subjects. Based on neural networks that replicate human thought processes, ML is an aggregation of complex algorithms that can distill abstract principles into tangible mathematical formulas. Concepts such as linear regression, clustering, and random forest structuring are examples of decision-making techniques that are utilized to make learning possible. Depending on who you ask, there are three general classes of learning models: Supervised, Unsupervised, and semi-supervised, each containing a multitude of hybrid alternative sub-models (reinforcement, temporal, etc.) within them. ChainGPT’s AI leverages all of the leading open standards in machine learning to optimize its model’s aptitude to evolve alongside ingesting new data. [**Disclaimer**](/misc/legal-docs/disclaimer.md) --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.chaingpt.org/overview/learn-the-concepts/machine-learning-ml.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.