First, you would take that language, then, you will write certain rules for that language and you’re going to implement the translator to translate from this language to the next language.
Now see if I ask you to do it for other language you’d have to start over.
You’ll have to start from scratch again and write the rules for the programming language.
But that is not the case with machine learning.
With Machine Learning, all you have to do is to actually train your machine learning model for translation from one model to the next.
Once your code is ready your model is ready.
And if you want to implement a new language translation all you have to do is to feed a data-set of that language and the machine learning model will take care of the rest.
So, you’ll be able to create a translation without even changing a single line of code.
Machine Learning produce predictionsMachine Learning is a paradigm in which we think about problem solving.
Also in machine learning we think more like a scientist, we observe, we run experiments and analyze experiment to form conclusions like we did in chemistry labs at school.
A breakthrough in Machine Learning would be worth 10 Microsofts ~Bill GatesNow this is somewhat different than usual programming (problem solving) but once you understand it, it becomes very easy to implement machine learning algorithms.
Artificial Intelligence creates ActionsNow, Let’s discuss about AI yeah Artificial Intelligence, it is just about making machines mimic human behavior i.
making computers as smart as humans like walking, talking, listening, reading, understanding and even learning new things.
Over the past few years computers have automated tasks.
For example identifying a cat in an image.
Now AI is not a new term.
Researchers have been trying to teach machines and give them human intelligence since 1960s that is 60 plus years since then researchers have tried many different techniques to program computers to mimic human intelligence.
The classical AI approach of using logical rules to model intelligence and understanding the world meant that giving precise rules and data structures created by researchers to implement those models.
But those methods never lasted longer.
They were wrong again and again.
But in today’s world, these modern techniques are slightly more advanced.
And they do represent similarity in which we learn.
Like, instead of explicitly telling a computer what we do is we make sure, we feed a bunch of data and machine learns from it, whether this is Cat or the dog you have to of course explicitly state that this image is of cat and this is of a dog.
But after giving lots of data of cats and dogs like What is cat or what is not a cat or what is not a dog.
A machine can fairly and easily identify with a reasonable accuracy which is a cat or dog.
Cats vs DogsSo talking about few terms we generally confuse them like ML vs AI vs Deep Learning.
Artificial Intelligence is a super-set of machine learning and deep learning.
Now AI is defined basically as a broad set of machine learning techniques and other things as well like symbolic reasoning and behavior based techniques etc.
Now Machine Learning is a subset of artificial intelligence, and Deep Learning is even a subset of machine learning techniques and There are a lot of machine learning techniques, so Deep Learning is one of them you can say.
In other terms, we can see the difference between Strong/ Weak/ Shallow/Deep Machine Learning algorithms.
Generally we listen and hear about strong or weak or shallow or deep machine learning algorithms.
Now weak and shallow ML algorithms are those which are specific to machine learning problem like identifying an image, Computer vision basically or natural language processing and strong or deep machine learning techniques are techniques which are generic machine learning algorithms where you can use the same algorithm for accomplishing multiple task.
To be Continued……If you liked and found informative, Don’t forget to give a Clap, Thanks.