Machine Learning For Absolute Beginners

In this blog, we will talk about Machine Learning, and types of Machine Learning.

Let’s start with the Machine Learning, We human are too lazy, we don’t want to do work, so we come up with the solution and we transfer our work to machine. And we have seen that machine have done it quite well with accuracy and speed. But machine do what we tell to do, that is what human needed much so far. But we are not satisfied with work because machine don’t have intelligence. So, machine can not do intelligent work. We can not tell machine to work intelligently because machine don’t understand the term intelligent So, First we have to define the term intelligent then we can transfer the work to machine that need intelligence. that’s a problem human can not be able transfer intelligent work to machine without defining the term intelligent to machine i.e what kind of intelligence is involved in particular work.

What is intelligence?

First we will understand the term intelligence, what does intelligence means, we human take information from the surrounding environment using five sense and process that information in mind and then trying to interpret this information to make some rules. On the basis of these rules, we make decision when we get into similar environment. If we make wrong decision then people will definitely say that you are not doing your work intelligently. Actually you didn’t process the information correctly and as a result your rules may be not good to make a decision. Suppose if there is raining, what will happened before raining, there is cloudy weather, humidity is 20, air pressure is 5, because of these reason it is raining now. And we make the rule i.e if weather cloudy equals to true, air pressure > 5 and humidity > 20 then it will rain. If next day it is not raining but conditions are humidity = 30, air pressure = 6 and weather is cloudy. But accordingly to the rule it should be raining. What happened? May be this rule is not good enough to make decision, may be we didn’t process the information correctly and may be we miss some other factors that is necessary to make a decision that should be any other factor. We have to find that factor and get the information and make rule in considering additional factor also. So that our error should be become neglectable.

Traditional Programming:

Traditional Programming, you just give the rules to computer and computer will give you the result just in above case you describe the rules by yourself through processing the information and then give this rule to computer and computer will do the rest and produce the output. But if that rule is not good enough then you have to make changes in the rules by yourself.
In Traditional Programming you give the rules, input is captured from the environment through sensor and program will produce the output i.e in above case tomorrow is going to rain or not.

Machine Learning:

We want to minimize human effort so, we want machine to learn rules by himself. We give machine, the input data just like human get the information, machine also get the information and trying to interpret the environment and makes some rules. How machine interpret the environment and convert the information into some useful rules. for this purpose we have different algorithms that get the information and trying to make some rules and these algorithms are called models. We use different model in different environment. The process of extracting rules from data in called training the model and when these rules used in order to process the input and produce the output this process is called Prediction.

Types of Machine Learning:

Mainly Machine Learning is divided into three categories.

• Supervised Learning
• Un-Supervised Learning
• Reinforcement Learning

Supervised Learning:

Before we go into Supervised Learning, Let’s talk about input data, we have two types of data label data, and unlabel data. label data is that data which is already tagged with the correct answer. Suppose you have data of previous one year of weather like you know the input data i.e weather cloudy, air pressure, humidity, and you also know the output of that day i.e it is raining or not. In unlabel data you don’t know the output of that day i.e is it raining or not.
In Supervised learning, you train the machine using data which is well “labeled.” And you try to map input data onto output using an algorithm and try to learn those rules or function that is converting into output.The goal is to approximate the mapping function or rules so well that when you have new input data that you can predict the output for that data. This approach is called task driven approach.

UnSupervised Learning:

In Supervised learning, you train the machine using data which is well “unlabeled.” And try make some rules on data by dividing data into categories. The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data. This approach is called data driven approach.

Reinforcement Learning:

Reinforcement Learning is the third type of machine learning in which no raw data is given as input instead reinforcement learning algorithm have to figures out the situation on their own. The reinforcement learning frequently used for robotics, gaming, and navigation. With reinforcement learning, the algorithm discovers through trial and error which actions yield the most significant rewards. So the purpose of reinforcement learning is to learn the best plan.