Human beings are intelligent because we learn from and accumulate our experiences.  However, more and more, I am impressed by a different form of learning – Machine Learning

Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed.

Machine Learning is a new trending field these days and is an application of artificial intelligence. It uses certain statistical algorithms to make computers work in a certain way without being explicitly programmed. The algorithms receive an input value and predict an output for this by the use of certain statistical methods. The main aim of machine learning is to create intelligent machines which can think and work like human beings.

Applications of Machine Learning
Following are some of the applications:
1. Cognitive Services
2. Medical Services
3. Language Processing
4. Business Management
5. Image Recognition
6. Face Detection
7. Video Games

Generally, Machine Learning problem can be classified into two broad  categories:



In supervised learning, we know the desired OUTPUT. We train the system with well-known data consisting of input and output. With the given data supervised learning algorithm infer a function, with help of that function it can able to provide output for over non-trained input value i.e. other than the trained input using the inferred function.

Let’s unearth this with an example: Consider you are going to buy a house near the beach. If you want to predict the price of the house, you provide the data such as (Input) size and price (Output) of the house which already existing near beach, then supervised algorithm will give you the estimated price (Output) of our house when you provide the data such as Size (input), with the function inferred from the existing houses.



Supervised learning problem is categorized into “Regression” and “Classification”.

Regression: Predicting result in continuous output over a large range. E.g.: Finding out the value of the house over a large value.

Classification: Predicting result using discrete values i.e. either 0 (this) or 1 (that). E.g.: Finding out Low blood pressure (Assume: 0) or High blood pressure (1) among the BP patient in the hospital. i.e. outcome within the set of outcomes.



Unsupervised learning is used where we didn’t have any idea about what the output looks like. This works by forming a various group based the characteristics of the given data. The group is formed by the data which holds similar characteristics.

Example: When you are on Facebook, you likely to see many of your friends(Sometimes enemy) face popping up in the Suggestion list. The reason is that you and your friend have many similarities such as same school, area etc. This similar characteristic makes you and your friends fall into the same group.


Unsupervised learning has different types, one among is “Clustering algorithm”.

Clustering Algorithm: Clustering is used to form a cluster of the object. The object within the cluster will have similar characteristics.

Example: If you type “google” in search engine, then the page is populated with the news related to google. What happens here is the search engine looks for the characteristics – google in each and every document and populate it in the search result as shown below:paint.png

This is an example of the clustering algorithm.

Benefits of Machine Learning
Everything is dependent on these systems.
  • Decision making is faster – It provides the best possible outcomes by prioritizing the routine decision-making processes.
  • Adaptability – It provides the ability to adapt to new changing environment rapidly. The environment changes rapidly due to the fact that data is being constantly updated.
  • Innovation – It uses advanced algorithms that improve the overall decision-making capacity. This helps in developing innovative business services and models.
  • Insight – It helps in understanding unique data patterns and based on which specific actions can be taken.
  • Business growth – With machine learning overall business process and workflow will be faster and hence this would contribute to the overall business growth and acceleration.
  • The outcome will be good – With this the quality of the outcome will be improved with lesser chances of error.

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