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Real-time applications of Machine Learning

Nowadays, humans are not able to fulfil any objective without the intervention of technology and the internet. Of course, Internet users are always satisfied with, impressed and even flabbergasted by the results they get and their accuracy as these technologies provide the user with what they exactly looking for. But, have you ever asked how this works and what are the behind-scenes factors that make this happen? Well, and to nobody’s surprise, this is a form of AI and machine learning. So, if you have little to no knowledge of Machine learning, this is the right article to read as we will go through this technology and its real time applications.

There are a plethora of AI-related implementations in the real world, Machine Learning is one of them. Machine Learning consists mainly in finding patterns and remembering them depending on the activity of the user. To put it in a more technical wording, Machine Learning algorithms use statistics with the intention of finding patterns in a big amount of data.

Basically, this AI application is divided into methods:

Supervised Machine Learning: In this method, the algorithms can apply what has been realized in the past to new information utilizing named guides to anticipate future actions. Beginning from the investigation of a known dataset, the learning algorithm creates a construed capacity to make predictions about the output esteems.

Unsupervised Machine Learning: it is utilized when the data used is neither arranged nor marked. The algorithms of the unsupervised machine learning study how frameworks can induce a capacity to depict a concealed structure from unlabelled information. The situation does not sort out the correct output, however it investigates the information and can make deductions from datasets to depict concealed structures from unlabelled information.

Semi-directed or Semi-supervised algorithms fall some place in the middle of unsupervised and supervised learning, since they utilize both marked and unlabelled information for preparing – commonly a limited quantity of named data and a lot of unlabelled data.

Reinforcement machine learning algorithms: this strategy permits machines and programming agents to naturally decide the ideal conduct inside a particular setting to amplify its presentation.

Now that the concept of machine learning and some of the aspects have been discussed, let us explore some of the real applications of Machine Learning. Netflix, a renowned movies and series broadcasting platform falls also under the category of Machine learning. The algorithms in Netflix gather as much data and information about you as they can so that they can filter and sort the movies and genre of series you like to watch. The data is collected based on your clicks and the names used in the search engine.

Image recognition is another real-life implementation of machine learning. In principle, the feature of recognizing faces is the same as how people recognize faces. While humans remember colours, shapes, phones and laptops store numeric data in their system to recognize the face.

Who among us does not know Google Translate? This application developed by Google was actually a solution to the language dilemma. In order to make Google Translate work, Google makes use of Neural Machine learning, that is the ability to store thousands of words, expressions and even sentences and render them into different languages.

In fact, what has been reached today was actually unreachable and even unthinkable. But here we are, thanks to the rapid advancement of technology, living in a world where humans let the smart machines do all the work.