Machine Learning is Gaining Popularity Again

Vincent Hughes June 30, 2017 Comments Total Views:14
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Machine learning has been in the news in recent times, but what exactly is it? Machine learning is a type of artificial intelligence that provides computers with the ability to learn without being clearly programmed.

Machine learning focuses on the development of computer programs that can change when exposed to new data. Both systems search through data to look for patterns and computational learning. Machine learning uses these methods to learn from the previous data and uses them for future data.

With the emergence of the internet, and the huge increase in the generation of digital information, stored, and made available for analysis, also contributed significantly to the rise of machine learning. Many machine learning programs have been around for a long time, but the ability to automatically apply complex mathematical calculations to big data repeatedly is a recent development.

The idea was that the machines will remember the things learnt and then use them in the coming events rather than to follow human instructions.

In normal program writing, commands are given in a language that the computer understands. We get the output on those instructions and the given input.

If machine learning is used in place of normal programming, then the result would be different. The result would be based on the input data and the system will gain experience by processing that input data. As it analyzes the data, it changes its programming as per newer demands. This leads to improvement in its accuracy also. It also means that machine learning gets better with the each input as the variables and parameters get changed each time.

Machine learning tasks are normally classified into three main categories. They depend on the nature of the feedback available to a learning system.

  • Supervised learning – The data is given a description and it is called labeled data. The computer is presented with this labelled data and their desired outputs, and the goal is to learn a general rule that maps inputs to outputs. This method is used to do future event predictions based on past data.
  • Unsupervised learning – This method is used for unlabeled data. Thus, the system knows nothing about the correct output and so, the program must find the correct output. It can do this by finding some structures within the data. This type of machine learning is perfect for transactional data. The factors of learning here are nearest.
  • Semi-Supervised LearningThis kind of machine learning method is used in similar instances, but it also uses unlabeled data while training. Unlabeled data is obtained naturally from the world but it does not possess any description. Usually semi-supervised learning works with unlabeled data more than labeled data, but it can use labeled data also.
  • Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal. The program is provided feedback in terms of rewards and punishments as it navigates its problem space. This method is used in gaming, vehicle navigation and robotics.

Applications:

  • Promedas, a medical software is a machine-learning-based program. It helps the doctors in detecting and diagnosing disease by making use of the data collected from previous records of disease patterns to identify disease in patients.
  • Amazon has automated the process of its employee access granting and revocation through a computer algorithm. The computer program uses the records to analyze the risks and how trustworthy an employee happens to be in general.

Conclusion:

The impact of machine learning on business is tremendous. It has opened new possibilities. The importance of effective data processing is also growing. For this, new machine learning methods have been devised. It has helped in accurate model making. It has also helped in taking better and smarter decisions quickly.

Effective machine learning becomes a difficult proposition due to difficulty in finding patterns and non-availability of enough training data.  Thus, machine-learning programs often fail to deliver the desired results.

 

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