I have recently started exploring the field of machine learning (ML). I think I understand the difference between ML and AI at high level, but I wanted to understand more accurately the differences between these commonly used concepts.
After some research, I came out with the following boundaries for these domains. Am I correct here or is something inaccurate?
AI :- AI is the intelligence given to machine to simulate the human intelligence like responding and behaving in a circumstance. It can include Machine learning, Natural language processing, Robotics, General intelligence or strong AI, Others. It's a superset of ML
ML:- ML is specific field of AI which helps to develop the program based on sample data(Training data) instead of hardcoding rules/programming like sum/multiplication. Basically machine learning is learning from past data and then predict/classify the given input. It can for example help in Recommendation engine/Fraud detection/Image processing etc
Deep Learning :- DL is further subset of ML and deeper than that. In ML , software upfront knows the features of training data and their output classify but in DL, algorithm itself identifies the relevant features/attributes of training data.
NN:- Neural network is specific group of algorithms used for machine learning that models the data using graphs of Artificial Neurons, those neurons are a mathematical model that “mimics approximately how a neuron in the brain works”. So we can say ML/DL uses the NN algo to do their work
Data Mining:- Data Mining is about using Statistics as well as other programming methods to find patterns hidden in the data so that you can explain some phenomenon. Data Mining builds intuition about what is really happening in some data and is still little more towards math than programming, but uses both. Machine Learning uses Data Mining techniques and other learning algorithms to build models of what is happening behind some data so that it can predict future outcomes
Here the source I used for identifying these boundaries: