Machine Learning is used in most critical applications, such as data mining, natural language processing, image recognition, and expert systems.In simple terms, Machine Learning solves those problems that cannot be solved by numerical means alone.
Let's know it deeply in the following sessions of the machine learning for absolute beginners.
What is Machine Learning?
In simple terms, Machine Learning is the ability of the system to learn something without beign explicitly programmed.
Want To Get Machine Learning Training From Experts? Enroll Now For Free Demo On Machine Learning Training.
Why is it matters?
Through Machine Learning it is possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster with more accurate results even on a very large scale. And by building these type of precise models, an organization has a better chance of identifying profitable opportunities to grow by avoiding unknown risks.
Where is it used?
Few examples of machine learning applications you may be familiar with-
- self-driving Google car
- Online recommendation offers from Amazon
- Quill.org-a writing instruction platform for schools making automated corrections on the errors made by students in writing like false word they type, comma misplaced, inappropriate conjunctions made etc.
- Malware file detection by Kaspersky
- Fraud detection by PayPal.
- In 2014, the algorithm has been applied in Art History to study fine art paintings,and revealed unrecognized influences between artists.
When do we use it?
When we want something to automatically apply on the complex mathematical calculations to analyse big data - over and over, faster and faster such that they produce reliable, repeatable results to make our decisions easy.
Who made it?
Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term "machine learning" in 1959 while at IBM
How does it work?
To make you understand how exactly Machine learning technology works, let me put you some real time problem in most simple words.
Assume you want to check for cat images in google. You want to use Machine Learning for this solution.
Now, some basic points about Machine learning systems.
They are made up of three major parts, which are: Model, Parameters and Learner. The basic definition is:
- A Model is the system that makes a prediction or assumption on solution.
- The Parameters are the signals or factors used by the model to make the right decisions..
- A Learner is a system that adjusts the parameters and in turns the model by looking at differences in predictions versus actual outcomes.
Now, let's come back to our example.
1. Making the Model.
Initially, It has to be given to the system by a human being,In our case, you will tell the system to assume that the most likely factors that might help identify what's a cat would look like in the images like colors, shapes and so on.
2. The Parameters given to the Model.
You feed in a training set of known pictures of cats along with some descriptions like cat has this shape, color, size, eye, tail etc. Now comes the learning part of the machine learning.The set of data that were entered by you is called as "training set" or "training data" because it is used by the learner in the machine learning system to train itself to create a better model.
3. The Learner
Now, learner try to figure out by themselves what an object is making?, initial group of colors, shapes and other features, then use the training data to refine that.then makes adjustments according to the data input given by you and validates with the training set and a new prediction is set to study for more time to earn that perfect score is projected. So every time a new input/ parameter is given, the learner is making small adjustments to the parameters and refining the model.
The first score are called real scores and second scores are called as revised scores. The real scores are compared against the revised model by the learner, those scores which are closer to the predictions will reshape the model. The cycle will keep repeating until there's a high degree of confidence occur in predicting the ultimate model.
