Data Science:

Data Science refers to the process of extraction of useful insights from data.

In order to mine big data, which is closely associated with the field, data science uses a diverse range of techniques, tools and algorithms gleaned from the fields.

Machine Learning:

In Machine Learning , statistical methods are used to empower machines to learn without being programmed explicitly.

ML is based on three key models of learning algorithms:

- supervised algorithms,
- unsupervised algorithms,
- reinforcement algorithms.

In the first model, a dataset is present with inputs and known outputs. In the second one, the machine learns from a dataset that comes with input variables only. In reinforcement learning model, algorithms are used to select an action .

Deep Learning:

In machine learning, data mostly passes through algorithms which perform linear transformations on them to produce output.

Deep learning is a subset of machine learning in which data goes through multiple number of non-linear transformations to obtain an output.

With deep learning, systems are learning to mimic human voices to the point where it is hard to distinguish between a human and a computer voice-over. Deep Learning draws us closer to giving computers the ability to speak like humans.