# What is Machine Learning

#### Machine learning is a method that uses data to teach computers to recognize patterns and key drivers, allowing them to predict future outcomes without being explicitly programmed.

<figure><img src="/files/h1qSLTNBA13W0FzCbkxY" alt=""><figcaption></figcaption></figure>

No-code machine learning is a simplified approach to machine learning that allows users to build, train, and deploy machine learning models without needing to write any code. This makes advanced data analysis accessible to non-technical users, empowering business teams to harness machine learning insights without relying on data scientists or programmers.

In no-code machine learning, platforms like Graphite Note provide intuitive interfaces where users can import data, select features, and train models through guided steps. For example, machine learning, as a method, uses data to teach computers to recognize patterns and key drivers, enabling them to predict future outcomes. In a no-code environment, this process is automated, allowing users to set up predictive models by simply uploading data and selecting key variables, all through a user-friendly, visual workflow.

By removing the complexity of coding, no-code machine learning enables organizations to leverage powerful data insights faster, supporting better business decisions and allowing companies to respond more quickly to market demands.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.graphite-note.com/graphite-note-documentation/understanding-machine-learning/introduction-to-machine-learning/what-is-machine-learning.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
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.
