We have all hearing these Buzz words recently, like artificial intelligence, machine learning, A.I. , Deep learning. 1
but there is a lack of clear distinction due to the tightly intertwined terminologies. 2
Let me share with simple definition, explanation and sharp differentiation between machine learning, 3
deep learning and artificial intelligence terminologies. 4
Here we can see we have this artificial intelligence, which is a technique that enables computers to 5
mimic human behaviors and the subset of these artificial intelligence. 6
We have machine learning. 7
We can define it as a subset of A.I. techniques which use statistical methods to enable machines to 8
improve with experience. 9
Under this machine learning, we have another subset, which is deep learning. 10
Deep learning is a machine learning, which makes the computation of multilayer neural networks feasible. 11
I hope you now understand better the difference between deep learning, machine learning and artificial 12
intelligence. 13
We are focusing in this course on machine learning. 14
It's a part of the computer field that involved from pattern recognition and computation learning in artificial 15
intelligence, which deal with building systems and algorithms that can learn from data without relying 16
on rule based programming. 17
Here, it's a very important point. 18
We need to make a difference between rule based programming, which is several lines, and a program 19
is just following certain lines and instruction. 20
Machine learning is working with sensors and sensing the environment. 21
Machine Learning System is able to make decision based on a new situation based on a new data. 22
If it's an industry, then they are hearing different sensors, they're receiving input and based on 23
the new situation, they can make a new decision. 24
If it's only a software program with new data, they can analyze the data based on the experience. 25
They can make a new decision. Unless the rule-based programming, which is only able to follow certain lines 26
of instructions. 27
There are several techniques to implement and to apply machine learning. 28
There are linear regression, K-means decision trees, random forest, PCA, super-vector-machines, 29
artificial neural networks (ANN). 30
In this course, we're going to focus on artificial neural networks.