Firstly, we need to know what is Machine Learning.

Machine Learning is a branch of Computer Science where it provides algorithms the ability to run and learn themselves from the data/experience it has. It does tweaks itself based on past experiences to find accurate results. It needs data that is used for structured/labeled form. It is a subset of AI(Artificial Intelligence).

Deep Learning is similar to Machine Learning but it has multiple layers of neural networks where each layer consists of algorithms. Deep learning algorithms can learn themselves without human intervention. This can have any form of data for training and it also results from It is a subset of Machine Learning which in turn is a subset of AI. Deep Learning is inspired by the human brain's neural network. It works similar to the human brain which makes it a trustworthy technology. If you need any kind of information on this article-related topic click here Machine Learning and Deep Learning

In Machine Learning algorithms a human needs to identify and hand-code the applied features based on the data type but DL doesn't need to have human intervention as it works with neural networks which is the same way how human brain interprets. It will learn and modify itself over time with the data collected. ML algorithms must learn to process by understanding labeled data and then use it to produce new results. However, if the result is wrong, there is a need for human intervention. Deep learning networks do not require human intervention, as multilevel layers in neural networks place data in a hierarchy of different concepts, which ultimately learn from their own mistakes. However, even they can be wrong if the data quality is not good enough.