A brief Introduction to Machine Learning

You’ve probably heard of machine learning and artificial intelligence, but are you sure you know what they are?
If you are struggling to make sense of them, you are not alone.
There’s a lot of buzzes that makes it hard to tell what science is and what science fiction is. Let’s get into it!

What is Machine Learning?

When it comes to machine learning it comes to a particular branch of computer science that can be considered a close relative of ‘ artificial intelligence.

Defining in a simple way the characteristics and applications of machine learning are not always possible since this branch is very vast and includes different methods, techniques, and tools to be realized.

Moreover, the different techniques of learning and developing algorithms give rise to as many possibilities of use that widen the field of application of machine learning to make a specific definition difficult.

However, we can say that when we talk about machine learning, we talk about different mechanisms that allow an intelligent machine to improve its capabilities and performance over time.

Machine Learning Examples in everyday life:

When we talk about machine learning, we often think only of applications in super-specific fields, in areas of research in science and medicine, space engineering or other branches not commonly understood by ordinary people.

This is a very common mistake, as machine learning presents many everyday applications.


What applies to marketing which has an even more important meaning in science?

The intelligent processing of big data considerably lightens the work of empirical research.

For example, thanks to self-learning systems, particle physicists can detect and process much more data and detect any anomalies. But machine learning can also be helpful in medicine.

Nowadays some doctors use artificial intelligence to make diagnoses and therapies. Furthermore, machine learning is also useful for the prognosis of diabetes or heart attacks.


Nowadays robots are now everywhere especially in factories: for example, they are used in mass production to automate ever-changing work steps.

However, these are not really intelligent systems, since they are only programmed to perform a single specific step. When self-learning systems are used in robotics they must be able to solve new tasks.

Naturally, these advances are of great interest also for other sectors: from space travel to domestic work, robots with artificial intelligence can take action in many areas.


The autonomous cars are a great showcase for machine learning.

Only automatic learning means that cars move independently and safely in traffic, instead of only in the test routes.
Since it is not possible to program all possible situations, autonomous vehicles must refer to intelligent machines.

For example in the form of artificial neural networks, analyze traffic and develop more efficient ways of managing it, such as by intelligently switching traffic lights.

Personal assistants

Even in their own four walls, smart computers are becoming more and more present: this is how normal homes become smart homes.

The Moley Robotics Company, for example, develops an intelligent kitchen equipped with mechanical arms that prepare meals.

Even personal assistants such as Google Home and Amazon Echo, thanks to which you can monitor systems and devices in your home, use machine learning technologies to better understand the needs of their users.

E commerce

E commerce is a virtual market place and using Machine Learning algorithms to understand the users behavior and interest. The technologies is growing along with the demand and new techniques of delivering product and service. E commerce is one of them emerging technologies using Machine Learning and AI.


Since the beginning of research on artificial intelligence, the ability of machines to play has always been a great stimulus for researchers.

The self-learning systems were put to the test in chess, checkers and even go the well-known Chinese board game among the most complex in the world, challenged by human competitors.

Video game developers also use machine learning to create more appealing projects.

Game designers can use machine learning to create a more balanced game experience possible and to make virtual opponents better adapt to the behavior of human players.

What are the career aspects of Machine Learning?

As mentioned above, Machine learning increases the performance of the machine and reduces the human efforts that in turn enable them to learn for themselves. So, there are career aspects of machine learning which are given below:

  • Machine Learning Engineer
  • Data Scientist
  • NLP Scientist
  • Business Intelligence Developer
  • Human-Centered Machine Learning Designer

Artificial Intelligence vs Machine Learning

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17 thoughts on “A brief Introduction to Machine Learning”

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