Machine Learning is not now a simple word or anything that one could just read it of and pass by. The concept has taken a wider and vast frame now and every business is welcoming it with both hands open. Till now, you might have heard more or less about machine learning as a concept where machines are learning to work like a human brain. The data scientists and engineers are nailing their heads to make a machine work as the human does. But have you thought about how all it could be possible? We mean, how a machine will be performing the role of a human brain? It’s quite amazing to envision it and above all, it is astonishing to know how this machines will learn.

Here in this blog, we are just trying to summarize the machine learning concept in a simple way (accepting the fact that explaining the entire concept is beyond our reach).

Let’s get into some more details:

Prior to any theory, just imagine the scenario-

You are looking for a coffee machine to buy online. So, how would you approach and would reach to the last decision of buying? A general approach would be- you will go to product specifications first then to the review section to know how the past buyers had experienced about it. If most of the reviews are about good and excellency of the coffee machine, you are looking for, then, you could end with the buying decision whereas if the reviews will show like bad, not good, the product is not up to the mark etc. showing that the previous buyers are not satisfied with the product, then, you might continue your search for a better coffee machine.

You might be thinking how one can relate this scenario to the machine learning. Let’s know the connection here. Everything will be done on your behalf by the machine.

A machine learning concept works on the pattern between the entities. The pattern that exists between the present buyer(you) and the past buyers to make the future sales (if you buy the same item). According to the pattern formed, the machine would take a decision on whether to make a purchase or not.

Machine Terminologies

  1. Input Data: Every possible data and information for the machine to get the results. Here, the previous buyers’ reviews are the input data for the machine.
  2. Output: Depending on the input data and every possibility of occurrence, the machine gives the result.  In the above case, a decision whether to buy or not will be the output.
  3. Algorithm: An approach that can combine input and output.
  4. Structured Learning: The algorithm works on the process to determine the output.
  5. Mapping function: The expression that is formulated in the algorithm to find the output. The function that will show the pattern and dependency of output on the input.

Let’s say- Y = f(x)


x is every possible information available as the input to the machine

Y is the output.

This function varies depending upon the input (data and information) and decision or target to be made in future. You should be aware of the fact that the predictions that have to be made by machine are unknown and machine learning concept approximates the predictions for the better results.


  • To make the machine work as a human, there needs to be a huge amount of data. In the scenario above, more reviews, number of confirmed buyers, the number of buyers who have given negative reviews, number of buyers of positive reviews etc.
  • There must exist a pattern between the available data and output that has to be drawn.
  • An algorithm that can connect the available data to generate a pattern to reach the conclusion.

Why do we need an algorithm?

  • It becomes so tough for you to accumulate each and every data and make possibilities out of it. Therefore, an algorithm will fit in the best way.
  • To make the machine work and to make it capable to reach the conclusion, we need an algorithm.
  • An algorithm that can derive the mathematical expression from the given information and data.

How does this algorithm work?

  • Depending upon the application and available data, the algorithm is chosen but the core aim is to derive a mathematical expression to form a pattern between the input data and output among the possibilities.
  • This algorithm works on the structural learning. A learning that structures every possibility and consequence to make the approximately an accurate decision.
  • Stronger the algorithm, the more accurate will be the decision (the machine will think more accurate as the human brain does).
  • For deriving the expression and combination between the available information, the engineers and data scientists use the specific programming language like Python, R, to make computers and devices work.

What the scientists put in the algorithm and code?

The code writers write a code for assembling all the data into the algorithm to build a capability in the machine to derive the relation between the input and output. Since the user’s behaviour cannot be predicted at any moment, therefore, the codes are written to teach machines to visualize and analyze the data to reach the conclusion.

We cannot formulate the exact relation between the available data and the predictions that have to be made and therefore we are programming the machines to learn from the available information, relate it with the consequences and possibilities, compute the available possibilities, derive the pattern and finally give a decision as much as same as the human brain does and that too within the smallest span of time. It’s going to be wonderful!


This is really a tough job to make machines learn and reach the output from the available raw data. No scientist has claimed that he has understood the human mind completely and therefore claiming that data engineers are now changing the machine to work as same as the human brain will not be true. Though their efforts are worth noticing and the data engineers are reaching their target closely.

A next step in the process of machine learning is the deep learning that creates neurons among the raw data and pattern and connects as in the human mind occurs while it thinks and joins the dots to make a decision.

Implementing machine learning in your business will be a sparkling idea that can give a 360-degree turn to your business in this technical moving environment.

Mvantage is one of the mobile app development company with the IT professionals having skills and passion for creating the human imagination services. If you have an idea, we have the innovative and prodigy team to convert it into reality.

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Harshit Khandelwal

Global Head – Strategic Partnership Harshit is a coffee addicted person. He loves exploring new ideas all the time. Harshit is an enthusiastic person who always shares his knowledge and experience with startups and progressive enterprises. He goes to any extend for his customers to solve their technology requirement.