what are the key difference of deep learning and machine learning

Learning Method: Machine Learning algorithms become better progressively as the number of data increases. They learn from the data to improve. On the other hand, Deep Learning algorithms learn from their own mistakes. Essentially, a neural network with three or more layers can learn to correct its mistakes.

Data Dependencies: Machine Learning algorithms work well with small and medium-sized datasets. However, Deep Learning algorithms require large amounts of data to understand it perfectly.

Processing Power: Machine Learning algorithms do not require high-end machines, a normal machine is sufficient. However, Deep Learning algorithms require high-end machines due to their complexity.

Feature Engineering: In Machine Learning, features need to be identified by an expert and then hand-coded as per the domain and data type. Deep Learning algorithms, on the other hand, try to learn high-level features from data in an incremental manner. This is a very distinctive part of Deep Learning which makes it very effective.

Interpretability: Machine Learning models are usually easy to interpret and understand. They provide clear information about which features are important for predictions. However, Deep Learning models are often referred to as "black box" models with their complex network architectures. It is difficult to understand which parameters are learning which features.

Execution Time: Machine Learning models take less time to train, ranging from a few seconds to a few hours. On the other hand, Deep Learning models take a long time to train, ranging from a few hours to many weeks.

Problem Solving Approach: Machine Learning uses various techniques to solve a problem, such as regression or decision trees. Deep Learning, on the other hand, uses a layered structure of algorithms called neural networks. There is less to design or customize in a neural network, as the layers learn from the data without much human intervention.