This repository contains file and programs used in the 'Machine Learning Workshop' for one day on 16 July, 2018. Topics covered
- Introduction to Machine Learning.
- Introduction to Data Science.
- Introduction to Python.
Before diving deep into let's see what exactly is machine learning. Machine Learning is a way in which, you are telling machine to learn something from the data given. Let's say for example, you go to Maths Class of Integration. At the first sight, you will not be able to understand anything by just seeing the equation. You will simply follow the rules as instructed by your teacher. That's exactly what happens when you make a normal code in any language. However, things start to change when you allow the machine to learn from the data itself. Now, for that matter it's very important to understand that the data should be clean enough such that machine should be able to learn from it rather than just confusing itself as to what is important and what is not.
Machine Learning is divided into 2 board categories of learning.
- Supervised Learning.
- Unsupervised Learning
Supervised Learning is when you know the input value what you are going to pass to the machine and you have the exact label also. Let's take an example - you went to fruit market and you asked the shopkeeper to give you some fresh bananas and apple. The shopkeeper tried to pick some oranges and at that moment, you asked him to only give banans and apple because you knew what does banana and apple look like irrespective of size and color (e.g. banana, apple can be of small or big size or even different color; you must have seen banana of green, yellow color and apple of green, red color). So that's what basically is supervised learning.
Unsupervised Learning is when you are not aware of the surronding values. You can simply classify the products using their properties. Let's take the same example - this time, you went to super market but this time you went to buy some avocados. Now, the shopkeeper might say that a certain fruit is avocado but how do you know? You have never seen avocado before. However, what if I say that I give a avocado and tell you that it's exactly how a avocado looks like, you will search the market for the exact or somewhat similiar fruit with those properties. So that's what unsupervised learning is all about.