There is a lot of media hype about all these terms but decoding the exact meaning of all these is difficult and the meaning of these terms changes from person to person. Here is my attempt on what all these terms mean

Basically, all these terms are kind of inter-related. See the diagram above.

Artificial Intelligence: In simple words, this is the capability of a computer to think. Think, just like we humans do. Self driving cars are a good example of AI. The car needs to think itself about how to handle traffic, human obstacles and reach the destination in shortest time and also in a comfortable manner.

Machine Learning: It is a sub area of Artificial Intelligence. It comprises of tools to learn from data. So, we can train a ML model with lot of dog pictures. The next time we feed a picture, the ML model would predict weather the picture is of dog or not.
More Examples:
1. Snapchat filters use augmented reality and Machine Learning for your flower crown selfies 😛
2. Predicting height of a person from his/her weight, food habit, city. (or something similar)

Deep Learning: It is all about Multi Layer Neural Network. When we talk about Google’s AI, we are actually referring to Deep Learning. Advances in Artificial Intelligences are happening in a subset of AI, which is called Deep Learning.

Data Science: All the above areas would require huge data (>1TB) to work with. Data Science has to do with retrieval of data from database, data visualization. It has nothing to do much with Deep Learning techniques.

What we need to know to learn AI/ML/DL?

I have been learning AI since last one month now, so I have little to no idea about core AI, but what I understood since last one month is the fact that, AI/ML requires core understanding of Linear Algebra, Probability and Statistics and Calculus. It is not enough if you know how to perform matrix multiplication, but it is important to know why we perform matrix multiplication and its practical significance. Similarly, you should know, the practical significance of partial and full derivatives. Sadly, almost all of us know how to compute maths, we hardly know its practical significance.

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Categories: Machine Learning


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