Machine Learning

Do you know about Machine learning and How Does it work?

Written by Christina Jones


In the exploding era of technology, it’s important not only for the engineers or students but for each of you to know the concept of machine learning. Such exponential expansion of data in the last few decades has made it possible to train machines by feeding them data. It was tried in the early 2000s also but due to lack of data, it was quite impossible to train a machine to precision. Now, with the introduction of big data machine training has become possible.

The key idea is more the amount of data to puts to train the machine, more accurate the result will come out to be. Very largely it has helped the computational finance, algorithmic trading in the trading world. However, the daily things that we see as the machine learning applications are AI code editor, AI camera, AI digital assistant, etc.

Do you know about Machine learning and How Does it work

It has become a key technique for image processing in face recognition and motion detection, etc. It is also a major tool in computational biology for the detection of tumors, drug discovery and DNA sequencing, etc. Automotive, aerospace, and manufacturing for maintenance use the computational efficiency of machine learning technology. Natural language processing, chatbots understanding your words also are the applications of machine learning.

More Data, More Questions, More accurate Answers.

What is Machine learning?

Machine learning is a method that provides an application of a system the ability to understand and learn to improve the user experience. The machine learning focusses on the way of learning from a dataset without explicitly programmed.

The learning starts with data analysis and observation such as direct experience, or instruction so that the algorithm can find a pattern in data and make better decisions in the future. However, if we talk about the main goal, it is to allow computers to learn automatically so that assistance and intervention can be decreased.

How Does Machine Learning Work?

Machine learning uses two techniques: Supervised learning in which the model is trained by feeding data so that it can predict future outputs. While another case is unsupervised learning in which the model finds hidden patterns in the input data.

Supervised Machine leaning

A supervised learning algorithm trains a model by taking a set of input data which upon training will generate future predictions to a new set. This is much similar to how a human learns. Supervised learning uses regression and classification methods to develop predicting models.

Classification techniques are used to classify the data into various categories or separate them into various classes. Suppose the case of Gmail, the algorithms of the Gmail engine move a mail to inbox or spam. Another example is handwriting recognition that uses a classification method that categorizes the letters and numbers.

Common algorithms for training based on classification includes bagged decision tree, k-nearest neighbor, logistics regression, neural networks, support vector machine (SVM), etc.

Regression Techniques go on predicting responses that are continuously happening like change in temperature or fluctuation in demand. One of the main applications of the regression technique is in trading.

Common algorithms for training based on regression includes linear and nonlinear model, stepwise regression, bagged decision tree, neural network, and regularization, etc.

Unsupervised Learning

As the name suggests, Unsupervised learning is opposite to supervised learning. Real applications have majority data that is unlabeled and uncategorized. This is what makes unsupervised learning an interesting chapter. Without any labeling, the algorithms are fed a lot of data and given the tools to analyze and understand the properties and the highlights. From there, it learns to cluster in an organized group in such a way that humans can conclude a sense out of the newly organized data.

The machine doing such great works for the organization of unorganized big data coming from various sources is really praiseworthy. Data analysis becomes much easy and classified once the data is organized. So, it enhances productivity resulting in big profits. Unsupervised learning can boost productivity to a big extent in several fields.

An example can be taken as what would if we had a large database with an accumulation of research paper ever published and had an unsupervised learning algorithm that knew the ways of grouping them is such a way that you would be aware of the current movement inside a particular domain of research.  Now think as you begin researching on a project yourself and the algorithms make suggestions to you meanwhile. In such a way, your productivity can be boosted to a great extent.

Take the example of Grammarly, an AI for removing grammar and punctuation errors while writing. How good it is to with someone who can guide you and suggest to you where you are mistaking. Grammarly is trained with unsupervised learning to such an extent that it can categorize your grammar errors, punctuation errors, and even unhealthy along with providing suggestions of the correct one as you move further.

Unsupervised learning can also be said as data-driven since it is based on data and its features.

Reinforcement Learning

It’s easy to establish an idea of differentiation based on labeling and the absence of labeling between supervised and unsupervised learning, but reinforcement learning is fairly quite different. Reinforcement learning is learning by mistakes. It will make a lot of mistakes in the beginning as you introduce a reinforcement learning algorithm to a dataset unless you provide some sort of signal that associates good behavior as a positive signal and bad behavior as a negative signal. Next, we can support the algorithms in any direction as we want whether negative or positive.

So, we can conclude that reinforcement is behavior-driven.


An idea about the machine learning concept is a good addition to your practical knowledge. Though there is no need to understand the deep algorithmic concept for you as normal people, you are having at least an idea about how daily things you use such as an AI camera or even a code editor for a purpose give you worthy suggestions. I came across many people demanding the basics of machine learning, so I thought it was my responsibility to make you understand some basic things.

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Christina Jones

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