Difference between AI, NLP, Data Science,Machine Learning & Deep Learning,

Artificial intelligence:

Is concerned with solving tasks that are easy for humans, but hard for computers. In particular AI is technology that enables machines to perform tasks which normally require human skills and intelligence.This is fairly generic, and includes all kinds of tasks,

• Recognizing Objects - Face Recognition, Location Recognition
• Recognizing Sounds - Apple’s Siri or Amazon’s Alexa
• Speaking - Text to Speech
• Translating - Google Translator
• Performing social or business transactions, 
• Creative work (making art or poetry), etc.

Machine learning:

MLis a part of data science and subset of AI.
ML is about enabling computers to learn (based on data ) and adapt to changing conditions. Or
ML is about applying Statistical Tool to explore & understand data.
ML has three approaches to do so: Supervised Learning, Un-Supervised Learning & Semi Supervised Learning.

• For e.g. Using ML, systems can made to learn by being exposed to different 1000s of reading formats and languages - This will eventually help machines to process future data more efficiently.  
•  Another e.g. Using ML, systems (Cars) can made to learn by being exposed to different moves and learn it's mistakes/success - This will eventually help to make driver-less cars,

When such ML algorithms are automated, it is called AI, and more specifically, Deep Learning.


Deep learning (ANN/CNN):

Is sometimes referred to as the intersection between AI and ML. Or Subset of ML.
Here we create Multi-Neural Network Architecture.
This it's about designing algorithms that can make robots intelligent, (Like mimic human brain which keeps learning and become more intelligent) such as:

• Face recognition techniques used in drones to detect and target terrorists, or 
• Pattern recognition 
• Computer vision algorithms to automatically pilot a plane, a train, a boat or a car.

Tools used are -

• A(Artificial) NN - Applied when data is in numbers 
• C(Convolutional NN - Applied when data is in images
• R(Recurrent) NN - Applies when data is in Time-Series

NLP (Natural language processing) 
Is simply the part of AI that has to do with language (usually written).

Data science
Is much more than Machine Learning though. Data, in data science, may come from a machine or mechanical process (survey data could be manually collected) and it might have nothing to do with learning.

But the main difference is the fact that data science covers the whole spectrum of data processing, not just the algorithmic or statistical aspects. In particular, data science also covers

• data integration
• distributed architecture
• automating machine learning
• data visualization
• dashboards and BI
• data engineering
• deployment in production mode
• automated, data-driven decisions

Easy Snapshot:

Hope this helps!!!

Arun Manglick