Separate these related concepts and understand how they fit together.
The core idea
Machine learning uses data to fit a model that makes predictions or decisions. Deep learning uses multi-layer neural networks for representation learning. Generative AI focuses on producing content such as text, images, audio, or code; many generative systems use deep learning.
A practical way to approach it
Start with the problem and available data. A tabular prediction task may work well with classical models, while language or image tasks may benefit from pretrained neural models. Define a baseline before selecting a more complex approach.
Try it in a small project
Use a small labelled dataset to compare a simple baseline with a more complex model. Keep training, validation, and test data separate, and record the metric that reflects the real task.
Common mistakes to avoid
A more complex model is not automatically more accurate or useful. Consider data quality, latency, interpretability, cost, and the consequences of mistakes.
Key takeaway
Keep the first version small, make your assumptions explicit, and verify the result with a repeatable test. For production use, check the current official documentation for the tools and versions you choose.