I've been lurking on these forums for years and honestly the terminology still gets me sometimes because it's basically like a square and rectangle situation where all machine learning is AI but not all AI is machine learning if that makes sense? Basically AI is the big goal and ML is the math stuff under the hood and I usually tell people to look at it this way: - AI tools are like the finished products you actually interact with like ChatGPT or Google Gemini where the "thinking" part is already done for you and you just give it prompts.
- ML tools are the building blocks like Scikit-learn or PyTorch where you have to feed it a ton of data and train it yourself which is way harder (at least that's what worked for me when I tried learning).
- Use AI when you need a quick solution or content and use ML when you have specific data and you want to build your own custom model thing from scratch. Does that help at all or am I just rambling? I'm still trying to wrap my head around the deep learning stuff tbh.

I’ve spent a decade in the weeds with this stuff and the distinction usually comes down to whether you're handling the training or the inference. Over the years, I’ve seen teams waste months building custom neural networks using PyTorch 2.1 when they could have just integrated an existing model. ML tools are the toolkits for building models from scratch, whereas AI tools are usually the finished systems that simulate reasoning. I remember back in 2018 trying to build a custom classifier for some niche sensor data... we spent weeks on the math. Today, most people just want the results without the linear algebra. tbh it's a lot easier now but the confusion is still there.

• If you need to predict a specific value from your own unique dataset, you need ML tools like Scikit-learn 1.3.
• If you need to process natural language or generate content, stick to AI tools like Anthropic Claude 3 Sonnet. Basically, dont build the engine if you just need to drive the car.