February 22, 2021 0 Comments Featured, Robotics & AI, Sticky

Machine Learning In Real-World: 5 Ways It Can Solve Problems


Machine learning is a division of artificial learning and, AI comes under data science. In data science, some algorithms are programmed to give outputs based on inputs. The input is the massive amount of data available and the output is a result of various statistical processing of the data that takes place in algorithms. Machine Learning is also the same with only one difference that being that the powerful algorithms adapt and learn as new data is available without any external programming. So with time, the accuracy of insights provided gets better as the machine continues to learn and adapt.

There many examples of machine learning in the present world. Websites that suggest one the advertisement based on their current searches and purchases or the various digital assistants that respond to one’s voice. In the medical field the image analysis systems that spot irregularities that escape the doctor’s eye. And of the major use of AI and machine learning can be seen in self-driven automobiles.


Machine learning styles

There are three machine learning methods (styles):

      • Supervised machinelearning

The training of supervised learning takes place on a labeled data set. A labeled data means that the data is prepared in such a way that it matches the data set the machine learning is built to recognize and further classify. Because the data is structured and labeled, the algorithm of the machine learning provides results very close to accuracy. So during training, the insights of the machine learning model are very close to the actual result.

One good thing about this style is that less training data is required when compared to other models because with the help of the labeled data the results are easily comparable with the actual results. The disadvantage being that labeled data is usually very expensive and the model is so based on the labeled data that it has a problem adapting to new information.

      • Semi-supervised machine learning

Semi-supervised learning is a step lower than the supervised learning method. In this, the labeled data set is used but at a smaller amount. This smaller labeled data will help them in setting a structure to classify and identify features from an unlabeled data set. It is the middle ground between supervised and unsupervised learning.

      • Unsupervisedmachine learning

Unsupervised learning uses a large amount of unlabeled data and, with the help of algorithms tries to extract proper features. And these features will help with classifying, sorting, and labeling the data when applied in real circumstances. This learning method is mostly used to find out patterns in data that can miss the human eye.



Cases where machine earning can be used

      1. Providing solutions in milliseconds

A human being can use its skills to process and generate answers for maybe a few hundred cases but, when there are millions of cases, it becomes humanly impossible. With machine learning, the large data set can be quickly processed and, the end-user will get a result in milliseconds. The effectiveness of management goes up without any human intervention.

      1. Massive data set but no patterns

One has access to a large amount of data with poor quality that contains many human errors, typos and the validation of records is not clear. Even if they can curate the whole data it is difficult to find patterns in it. This problem is solved by machine learning. Using the algorithm, it will find patterns and connections that will help in interpreting the data properly.

      1. When coding cannot solve the problem

Human tasks like recognizing spam emails can be quite difficult as the factors that determine whether it is spam or not are a lot. So continuously changing the coding and rules to fit in the factors can be quite difficult. Machine learning will run the algorithms and without any human intervention, extract the necessary patterns automatically.

      1. Manually possible but at a higher cost

Many tasks are humanly possible and may even take few minutes to get the outcomes, but are not cost-efficient. Allocating huge manpower to just one task is not efficient management and thinking. But on the other hand, by using machine learning, the cost spent on setting it up will justify the scale on which it can be used.

      1. Adapting to changes

Everything is very dynamic in the current circumstances which, require quick adaptation. Delays can cause problems. Humanly adapting to everyday changes is a bit difficult. But machine learning can adapt itselfcontinuously to new data sets. Such a feature is very useful to solve problems in the long term.


As big data keeps growing and, data scientists continue to develop more capable and powerful algorithms, machine learning will become more and more efficient. And this efficiency will help in solving many problems with accurate predictions and insights. And the accuracy will make both the private and work life more and more effective when it comes to problem-solving.