| Base of Difference | Deep Learning | Machine Learning |
| Data Dependency | When we say about the Deep Learning here we comparatively need large amount of data is needed. And increase in data will also increase the performance. | When we say about the Machine Learning we need sufficient amount of data which can build a good model. And if data goes on up level will not improve th performance. |
| Hardware Dependency | Here when we say about the hardware requirment here we need High-end machine. | Here in machine learning we can also work on small end machine. |
| Approach used | When we say about the approach used in Deep learning here problem are solved in one go by using several layers of neurons. | Here when we say about the approach, here we will subdivide the large problem into several small tasks and in the end, are combined to build the ML model. |
| Time Need for Execution | When we say about the Deep Learning we need more time for execution because a number of neurons use different-2 parameters to build a model. | When we say about the Machine leaning here Comparatively, less execution time is needed in the case of ML. |
| Featurization | Here in Deep Learning we learns from the data itself and does not need external intervention. | Well in Machine learning, external intervention is necessary to provide the right input. |
| Interpretation | Here in Deep Learning it is hard to interpret the process of solving the problem. Because several neurons collectively solve the problem.
| When we say about the Machine Learning it is easy to interpret the process in machine larning model. And it has logical reasoning behind it. |