Deep learning and machine learning should be distinguished. It is necessary to understand that deep learning is machine learning. In fact, it is an evolution of machine learning. Deep learning uses a neural network which is programmable. It enables machines to make accurate decision or make accurate predictions without help from humans.

Machine learning, by definition, is an application of artificial intelligence (AI). It includes algorithms which parse data. Then they learn from data. They then apply what they have learnt to make informed decisions. Machine learning involves a lot of complex math and coding. By machine learning, a function is performed with the given data, and this gets better over a period of time. Machine learning facilitates all types of automated tasks.

Deep learning model analyses data continually with a logical structure. It is a layered structure of algorithms called artificial neural network inspired by the biological networks of neurons in the human brain. It is tricky to make the model draw correct conclusions. To make it flawless, there is lot of training involved.

However, when it works as desired it is magical. An image recognition application can identify flower, fruits, animals and human beings. It can recognise speech and can translate and can drive autonomous cars.

Though deep learning is a subset of machine learning, both function similarly, and hence the terms are used interchangeably. However, they have different capabilities.

Basic machine learning models progressively improve while performing specific functions in the light of new data. However, they still require human intervention. In deep learning, an algorithm decides whether the prediction is accurate or not. No human help is sought.