AI vs ML vs DL vs DS

The most fundamental question of data science is what the basic difference among AI(Artificial Intelligence), ML(Machine Learning), DL(Deep Learning) and DS(Data Science). Many of us have confusion regarding this.

AI(Artificial Intelligence) enables the machine to think so that without human intervention the machine is capable of making its own decision. This is the final goal of an AI application.

Machine Learning is the sub-set of AI. It provides us with a statistical tool to explore and analyze the data for understanding. It has 3 different approaches:

   

Supervised ML:	Using this, Prediction is done on basis of past labeled data
Unsupervised ML:	It provides us the clustering technique . Base on similarities of data, it tries to group the data together. Hierarchical clustering,K-means clustering,K-NN (k nearest neighbors),Principal Component Analysis,Singular Value Decomposition,Independent Component Analysis
Reinforcement/ Semi Supervised ML:	This approach is used when some part of data is labeled and some are not labeled.

Deep learning: Sub-set of Machine Learning: 

It makes the machine learn to think like how the human brain does.

Multi Neural Network Architecture is used for the purpose. Its main target is to mimic the human brain.  Different techniques sued for this are:

ANN	If Data Input is in numeric form.
Convolutional Neural Networks (CNN)	If input is im images, videos .. It generally used in the field of computer vision.
RNN	If input is in form of time series form of data

Data Science: Its a technique tries to apply all the above processes. Statistics, Probability, Linear Algebra, Differential Calculus. All the above things will have to be learnt for becoming a data scientist.  Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.[1][2] Data science is related to data mining and big data.