What is the difference between Machine Learning and Deep Learning?

Machine Learning is a technique to learn from that data and then apply what has been learned to make an informed decision.
The main difference between deep learning and machine learning is, machine learning models become better progressively but the model still needs some guidance.
If a machine learning model returns an inaccurate prediction then the programmer needs to fix that problem explicitly but in the case of deep learning, the model does it by itself.

Machine learning:

Machine Learning can perform well with small-size data.
Machine learning can work on some low-end machines.
Features need to be identified and extracted as per the domain before pushing them to the algorithm.
ML is generally recommended to break the problem into smaller chunks, solve them and then combine the results.
In ML, Training time is comparatively less.
In ML, Results are more interpretable.
No use of Neural Networks in Machine Learning.
Solves comparatively less complex problems in Machine Learning.

Deep Learning does not perform as well with smaller datasets.
Deep Learning involves many matrix multiplication operations that are better suited for GPUs.
Deep learning algorithms try to learn high-level features from data.
DL generally focuses on solving the problem end to end.
In DL, Training time is comparatively more.
In DL, Results may be more accurate but less interpretable.
Uses Neural Networks in Deep Learning.
Solves more complex problems in Deep Learning.