Machine Learning vs Deep Learning vs Artificial Intelligence

While terms like AI, ML and DL have been in the market for quite some time, a common mistake is to use them pretty much interchangeably

Machine Learning vs Deep Learning

Machine Learning (ML) and Deep Learning (DL) both have common objectives: To generalize and make predictions about new samples.

Both ML an DL is about fitting the functions to the samples (training data). But in case of DL, the functions are connected layers of nodes. Deep learning machines don't require a human programmer to tell them what to do with the data unlike typical ML algorithms (supervised learning). That being the reason Deep Learning requires much larger sample data in order to achieve the objective (prediction/classification) .

Deep Learning in essence is a subset of Machine Learning. Example of some Deep Learning scenarios could be filling colors to black and white pictures, detecting traffice lights for self driving car etc.

Deep Learning algorithms are hence typically much more complicated

Artificial Intelligence

This is really the universal set; the broader umbrella intended to mimic human intelligence through various mechanisms like Machine Learning, Deep Learning , Computer Vision , Cognitive Computing