ModelRefs / Getting Started With AI

Getting Started With AI

A guided starting path for developers learning AI and modern LLM engineering.

Overview

A guided path into AI and LLM engineering for developers, starting from Python and ending at a trained neural network.

No AI or ML experience is assumed. The tutorials carry runnable Python, so the path is read-and-run rather than read-only.

What you will learn

  • Python Fundamentals

    The language features every later step assumes: data types, control flow, functions and the I/O primitives used by every model SDK.

  • NumPy and data processing

    Arrays, slicing and broadcasting - the numerical layer underneath every ML library.

  • Machine Learning basics

    Supervised learning, model fitting and the metrics that judge a result.

  • Real-world projects

    Putting the pieces together on problems that resemble production work.

Prerequisites

  • Basic programming knowledge in any language is helpful but not required.
  • High-school mathematics: algebra and basic calculus.
  • No AI or ML experience required - the path starts from scratch.
  • No local setup needed to begin; the tutorial snippets run in the browser.

The path, in three steps

  • 1. Foundations

    What AI is, Python basics, and NumPy fundamentals.

  • 2. Machine learning

    What ML is, linear regression, and classification.

  • 3. Neural networks

    An introduction to neural networks, then building a first one.

Continue your research

Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to Getting Started With AI.