Machine Learning vs Deep Learning

We all know what Machine Learning (ML) models are right? ML is about using raw but structured or semi-structured dataset, then creating feature tables which are no more than traditional tables but with cleansed data. Later, ML models use this cleansed dataset to classify data and produce expected results.

Same way, Deep Learning models is about using raw but unstructured dataset where no one will know what will flow in. Deep Learning models dig out required data from that unstructured dataset to prepare cleansed dataset in shape of feature tables to classify for expected results.

For example, if we need to find out how many cars, bikes or buses there are in structured or semi-structured datasets then it’s relatively easy as in rows and columns format. We can have the width, length, and height so using ML models can easily ready this dataset and produce expected output. But what if we give pictures to ML models to extract the same result, will it be possible? The answer is No as ML models normally work on structured or semi-structured datasets. To find out cars, bikes, or buses from pictures, Deep Learning (DL) algorithm and models are used which can recognize the objects in the pictures and find out which presents car or bike or a bus. Please note, the next stage is supervised and unsupervised learning.