That's a great question. Machine Learning is the overarching space where deep learning is a subspace of machine learning. So if you grasp some basic concepts of machine learning, then you can apply them to deep learning.

All the exciting innovation over the past 13 years comes from deep learning mainly in working with images and natural language.

Machine learning is good for tabular data problems, particularly decision trees, that work well to reduce uncertainty for business outcomes, like sales and marketing as one example.

Machine Learning Basics:

Linear regression - Y = Mx + B (predicts a future value) Classification (logistic regression) - Y = 1 / 1 + e^-(b0 + b1x) (predicts probability of a class or future event)

There is a common learning process between the two called gradient descent. It starts with the loss function, that measures the error between predictions and ground truth, where you backpropogate the errors as a feedback signal to update the learned weights which are the parameters of your ml model which is a more meaningful representation of your dataset that you train on.

In deep learning it's more appropriate for perception problems, like vision ,language and time sequences. It gets more complex where you are dealing with significantly more parameters in the millions, that are organized in hierarchical layer representation.

There are different layers for different types of learning representation, Convolutions for Images and RNN for Sequence to Sequence learning and many more examples of layers, which are the basis of all deep learning models.

So there is a small conceptual overlap; but I would say deep learning has a wider variety of interesting applications, is much more challenging to learn, but not impossible by any stretch.

There is no harm in giving it a try and diving in. If you get lost and drown in complexity, start with machine learning. It took me 3 years to grasp, so it's a marathon, not a sprint.

Hope this helps