Machine Learning in a simple language to a Child
Have u ever noticed how the “recommended/recommendations/for you” page flashes in your most commonly social media platforms like youtube, Instagram, Facebook? or the recent searches in google appears. I will tell you its the whole magic of machine learning. It has become a very important part of our lives.

First, What is Artificial Intelligence?

“Artificial Intelligence” teaches a machine how to work or perform actions or behave like human beings.

Now, What is Machine Learning?

Machine Learning

Machine Learning is a part of “Artificial Intelligence”. Machine Learning is basically a combination of two words “Machine” and “Learning” which means “Machine is Learning”.

Machine Learning is a little bit different from Artificial Intelligence. In the case of Machine Learning, the machine learns itself from its data and for that, we have to feed a lot of Data in our machine. Then from its own, it modifies it according to its need it makes it better and then it applies it in the real world.

for example,

Feeding data in Machines

I took a little boy (think it as a machine) to a Zoo there I showed the boy a parrot and a cow and then I told him that whichever has wings is a bird and whichever don't have wings is an animal(its like providing the data to the machine) and then I showed him a peacock and a tiger so he will automatically be able to distinguish that which one is a bird and which one is an animal that's how Machine Learning works by feeding the data as much as you can so that the machine can learn and develop more from it.

Now, focus on how the machine determines the same shape/kind:

Different Patterns for Birds

Machine Learning is entirely based on “patterns” it means similar structures like if we take the above example of parrot and peacock, how the machine determines that both are birds because they both have wings(that acts as a pattern) among all the data it only selected that is a bird because of the pattern or kind of similar structure of bird as provided to the machine( as data).

And now coming to “clusters” these are the similar collections of patterns that help to analyze the data and process it.

Now, the Key Points On Machine Learning:

Data -> Information:

In Machine Learning the only thing we can provide to the machine is the data but the problem is that the data can be in the form of anything like image, video, text, or any file and then we have to convert the data to information.

Like, for example:

Cream and Cone

There is a little boy who doesn't know what is an ice cream then we will tell him that whichever thing has a cone and in top of it a sweet cream and its cold(these are the data but not given in a proper format ) then the thing is an ice cream (this is the information). Look we have just converted those data into information.

2.Problem-solving Tool/Optimise Performance criteria using Past Experience :

Through Machine Learning we can develop those kinds of tools which can solve any kind of problems from its past experience.

Like, for example:

Boy Learning Chess

If a boy is being taught how to play a chess game at first he will lose because he is new but if he plays it again an again then he will be familiarised by the opponents way of playing the game then he will not do his previous mistake this is called learning from past experience it is one of the most important things in Machine Learning.

Types Of Machine Learning:

1.Supervised Learning:

It is basically a type of Machine Learning in which the system knows the desired output. If you get the desired output, in the end, your learning is successful otherwise keep on trying.

Like, for example:

Supervised Learning

Imagine a kid who doesn't know anything and we have to teach him alphabets then by showing pictures of the alphabet we will teach the kid and eventually he will be able to distinguish between each and every alphabet. This is Supervised Learning in which you have a teacher and you know the output.

2.Unsupervised Learning:

It is basically a type of Machine Learning in which the system doesn’t know the desired output. It's like the machine will try to make a pattern from the input provided to the machine and after trying and practicing it will form a pattern then also it is not sure whether the output is correct or not.

Like, for example:

Unsupervised Learning

If you teach a boy how to make an alphabet “A” its impossible for him to draw it without any steps that's why at first we teach kids to make two slant lines, then one straight line, etc.. then finally an alphabet “A” is made then also the kid is not sure whether he is right or not. This is Unsupervised Learning in which you don't have a teacher and you don't know the output.

3.Reinforcement Learning:

It is basically a type of Machine Learning which can be called both as “REWARD BASED” and “FEEDBACK ORIENTED”.

From “REWARD BASED” learning we can understand that we will get some rewards for performing actions it solemnly depends upon the programmer's decision on how he set the rewards for a particular set of actions.

Like, for example:

REWARD BASED-Giving Reward for good work

We tell children that if you will score good marks then you will get gifts those gifts(entirely programmer's decision) are called Rewards.

From “FEEDBACK ORIENTED” learning we can understand that we are performing some activity and on those activities, we get some feedback which either can be positive i.e., Activity Successful or negative i.e., Activity Unsuccessful it solemnly depends upon the environment.

Like, for example:

FEEDBACK ORIENTED-Giving Feedback in a Restaurant

If you go to a restaurant then after paying the bill they will ask you for the feedback if you enjoyed their service then you will give positive feedback and if you didn't enjoy then you will give negative feedback as you can see it entirely depends on the environment of the restaurant and the staffs.

Top Companies that use Machine Learning:

GOOGLE GBoard By Machine Learning

GOOGLE: Google is regarded by experts to be the most advanced company in the field of AI, machine learning and deep learning. The Google Assistant, recent searches, word recommendation, etc the things which google has developed made our lives very much easier.

Face Recognition in Facebook By Machine Learning

2. FACEBOOK: Facebook is also the finest Machine Learning example the auto tag, recent searches, suggestions friends, etc. These all are only possible through Machine Learning.

Speech Recognition in Baidu By Machine Learning

3.BAIDU: Another company that has been very active in the acquisition scene is the Chinese search giant Baidu. The company is particularly interested in natural language processing, with a view to developing a workable voice-activated search function.

Conclusion:

We can conclude that Machine Learning does three things at a time at first it analyses data, then gives a response also and gives the problem a real-life figure.
['Bhavna Kumari']
['Machine Learning', 'First Post', 'Computer Science', 'Artificial Intelligence', 'Beginners Guide']