INTRODUCTION TO MACHINE LEARNING | WHAT IS MACHINE LEARNING? | WHAT ARE THE TYPES OF MACHINE LEARNING? | NOOB CODE PRO

 Heyy there!!! How is it going? I have quite an interesting topic for you today ;) Today, we will learn a bit about machine learning. Don’t worry there is no difficult or complex code in this lesson, this is just an introduction to machine learning for beginners. This article covers what is machine learning? And what are the different types of machine learning algorithms? Let us get straight to it ☺

WHAT IS MACHINE LEARNING?

In machine learning, we create a software which can learn from a data/example. You don’t have to add logic every now and then based on the different scenarios the algorithm may come across. Based on the data it is given, the algorithm will learn the logic by itself.

So if the algorithm learns that 2+2 = 4, the next time you provide it with some unseen data like 2+3 = ?, it will be able to predict the answer. The algorithm looks for patterns in the data and the output provided to it and uses that to predict the output of unseen data. We are basically training a machine to think and act like humans.

Let us take the following example to understand this a bit better:

INPUT 1	INPUT 2	OUTPUT
1	3	4
2	2	4
3	2	5
4	7	11
5	6	11
8	5	13
9	8	17

We as humans can clearly see the pattern here, right? We are adding the numbers from columns 1 and 2 to produce our output. Now, we want our algorithm to learn the same thing. So we provide a machine learning algorithm with the above data, and the algorithm splits the data into two parts, training data and testing data. 

The training data is used to train our model. Basically the algorithm will try to figure out the pattern between the input and the output with this data.

The testing data is then used to test the algorithm’s logic. It basically checks if the algorithm figured out the right pattern. If it did, your model is now ready to be deployed on some unseen data.

TYPES OF MACHINE LEARNING:

Let us take a look at the different types of machine learning:

1. SUPERVISED MACHINE LEARNING:

Supervised machine learning uses explicit learning which includes data, input and output. Basically, this is when you are giving examples to the algorithm to create a logic. For example, if you want to teach the algorithm how a cat looks like, you will show the algorithm a cat’s photo and actually tell the algorithm that it is a cat. This way you are training the algorithm to recognize cats by actually telling it what a cat looks like.

In simple words, in supervised machine learning you will provide both the input and the output to train the model/algorithm.

2. UNSUPERVISED MACHINE LEARNING:

In unsupervised machine learning, you give the algorithm the data and it is totally upto it how it classifies and categorizes the data. It puts similar data together. In Unsupervised, it finds a pattern in input data with no associated output. There is no correct answer or supervision provided. It groups/clusters the data. In unsupervised, in a data it tries to find the best cluster for the data. 

Let’s try to understand unsupervised machine learning with a real life example. When you go to a supermarket and see two products placed next to each other, it is because the people who were interested in buying the first product would usually buy the second product as well.

3. REINFORCED MACHINE LEARNING:

In reinforced machine learning, the algorithm learns from seeing the results from an action in an environment. This is an approach used in A.I. The environment gives the algorithm a reward based on the action it performs.

Let’s take an example of robots playing football/soccer (Yep!! that is an actual thing). So when a robot tries to score a goal and misses, the distance by which he missed the goalpost is what the reward is based on. So let us say the robot misses the goalpost by 15 cm, the robot will be rewarded by the environment, Now, it will try to improve its reward, so the next time the robot tries to score a goal it misses by only 5 cm and gets a better reward. Now it knows that it is doing the right thing, so it tries again and finally scores a goal and gets the best reward.

CONCLUSION:

This is quite interesting, isn’t it? This is why I absolutely love machine learning. Teaching machines to think and act like humans is just fascinating. Hope you guys found this knowledge as helpful and interesting as I did when I first came across the whole machine learning concept. 

Do you think machine learning is the future? Let me know your views in the comments below, I would love to go through it ;) If you found this helpful1, leave a like and follow NCP for more content like this.

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