Beginners sometimes treat natural language processing (NLP) and machine learning (ML) as two options to choose between. They are not rivals - NLP is a specialisation that sits on top of machine learning. That relationship answers the ordering question almost by itself.
The relationship, plainly
Machine learning is the broad discipline of teaching systems to learn from data. NLP is applying that discipline to human language - chatbots, translation, sentiment, the tech behind large language models. You cannot do NLP well without the ML foundations underneath.
So learn ML first
- ML gives you the concepts everything else builds on
- Much of what makes NLP work is just ML applied to text
- Trying NLP first means constantly hitting gaps you have to backfill
Start with classic machine learning, which most beginner routes teach first for exactly this reason.
When to branch into NLP
Once you are comfortable with ML fundamentals and have done a project or two, specialise into NLP if language problems interest you - it is one of the most in-demand areas thanks to generative AI. By then you will have the maths and coding to move fast.
If you only want to use language AI
You do not need to build NLP to use tools like large language models effectively. Understanding what they do and where they fit is a different, faster skill - the focus of Strategic Application of AI in Business.
The bottom line
Learn machine learning first; NLP is a specialisation built on it, not an alternative to it. Get the foundations, do a project, then branch into language work if it appeals. Related: best way to start learning AI in 2026.