Machine learning and artificial intelligence are closely related and it’s not wrong to say that the abstraction level of between these two words is fairly very thin and they can be interchangeably used.
What is machine learning?
Machine learning and artificial intelligence are the branches of computer science and if anyone is doing Masters or Ph.D. then they must have studied this in their curricular as well. But according to me machine learning is closely related to data mining hand you have been already using it very much in your life.
Artificial intelligence is completely a different thing. If you are not aware of machine learning then you might already be aware of data mining, evolution of data mining has been there since the evolution of data in computers which have been into the world quite a lot.
You can better understand what is machine learning by an example. We all use email or email service provided by Google called Gmail. In your inbox or email you will see a spam section and if you don’t know what is this section then we will help you get the complete info.
You will see some of your emails are in your inbox and some of them into the spam box, now what is this how Gmail categorise or identify the spam emails from the good emails? This is all done by machine learning whether that is data mining. There is a huge amount of data and programs and algorithms which is designed in such a manner so that it can predict that whether this email is spam or is it a good email that needs to be delivered in your inbox.
It is not always perfect, sometimes good image also land in the spam section and spam emails is shown in the inbox, this is a very good example of machine learning at a very small level.
This is version 1 of machine learning and now what we are seeing in our day to day life is he version two of machine learning.
What needs to develop Machine Learning application?
There are a lot of things comes under the machine learning and first of them is data sets, data set can predict a lot of things, for example, if I just show you a chair there you can say that it’s a chair but if I say that that’s a wooden chair and other one is glass chair, then you can see the difference between these two chairs.
And if I ask you to write a program for that then it is not an easy task, let’s take an example if you are writing a program that it should have a four legs and a wooden texture that would be a chair but, when I say that it can be just a circular chair having a circular top with a square back and a single pole which is connecting with the another circular base that is also a chair but you cannot write a program for that. And for that situation, we require a huge number of data sets.
The second thing that data set is being fetched to something known as classifier, it is just an algorithm which can determine the output based on whatever the data is being fetched and as you all know the more data we are going to have the more predictions capability is going to be there.
So, now based on what kind of data you are supplying your classifier can classify the image of any other thing. In our example we are just taking an image of chair so it can predict that image of chair with some certain amount of confidence that it can be a chair. It can never be hundred percent sure but it’s always about the ratio of how much confidence that its showing that the object is 99% or 80% or 70% chair.
In simple words, we can say that a machine learning needs a huge number of data sets, being given to classifier and based on that data set, it processes and show the results.
There is an another example you saw, Google’s new product (Google Lens) just you can open your camera app and see the restaurants name by the camera and based on handwriting predictions, image predictions and the logo prediction then it can get back to the huge amount of data set that is presented in the Google and then find out the ratings and reviews of the restaurant.
There is an another example of machine learning which is an application which predicts you how you will look in your it is over how you look while you are a kid this all works on machine learning too. The name of the application is FaceApp.
How you can get started in machine learning?
There are couple of ways of getting started in machine learning. Machine learning is first of all dependent quite a lot in mathematics but not all the time it’s going to be like designing your neural networks or designing your patterns.
The first language you need in order to get started with machine learning is Python. This language is heavily used in machine learning now. If you are still confused language you need to choose to be a programmer first then check this post.
In Python you will learn all the basics of programming, Python is the one way of getting starting with machine learning, that’s not the only way you need other languages to write a successful code for machine learning, it all depends on how much data set you are having a how much data you can collect, its heavily dependent on that.
Now another thing you need to know about Machine Learning how you do the implementation of machine learning. If you are using an apple machine like Macbook, iMac or Mac Mini you can get started, with a new kit called ML kit, which is machine learning. All you need to do is patch it a data set and it can produce result. It is very very easy to implement and use. Apple is trying hard so that everybody get an access to machine learning and everybody is able to design applications.
Python and Mac OS’s ML Kit are the two good ways to get started in machine learning.