It is a common worry: does learning machine learning mean diving straight into deep learning and neural networks? No. You can - and generally should - learn classic machine learning first, and a great deal of valuable, paid work never uses deep learning at all.
Two different things
- Classic machine learning - regression, decision trees, clustering and similar. Interpretable, efficient, and the workhorse of most business problems.
- Deep learning - neural networks powering image, language and generative models. Powerful, but heavier and not always the right tool.
Treating them as one thing scares beginners off unnecessarily.
Why start with classic ML
- It builds the core intuitions all of AI rests on
- It needs less data, compute and maths
- It solves a huge share of real problems on its own
- It makes deep learning far easier to grasp later
Most beginner courses, including the recommended starting routes, begin here for good reason.
When to add deep learning
Move to deep learning when a problem needs it - unstructured data like images or text, or generative tasks. Until then, classic ML is not a lesser path; it is often the correct one.
If you only want to apply AI
You may not need either in depth. Understanding what these methods do and where they fit can be enough - the focus of Strategic Application of AI in Business.
The bottom line
Yes, learn machine learning without deep learning - start with classic ML, which is the right foundation and enough for much real work. Add deep learning only when a problem calls for it. Related: NLP vs machine learning, which to learn first.