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How Machine Learning Solves Big Data Challenges?

Author: Jaykumar Jain
by Jaykumar Jain
Posted: Aug 25, 2019

Machine Learning is a branch of computer science, a field of Artificial Intelligence. It is a data analysis method that further helps in automating the analytical model building. On the other hand, as the word demonstrates, it gives the machines (PC frameworks) with the ability to gain from the information, without outside assistance to settle on choices with least human impedance. With the advancement of new advances, AI has changed significantly in the course of recent years.

Let us Discuss what Big Data is?

Big data means too much information and analytics means analysis of a large amount of data to filter the information. A human can't do this task efficiently within a time limit. So here is the point where machine learning for big data analytics comes into play. Let us take an example, suppose that you are an owner of the company and need to collect a large amount of information, which is very difficult on its own. At that point you begin to discover a piece of information that will help you in your business or settle on choices quicker. Here you understand that you're managing tremendous data. Your examination need a little assistance to make search effective. In the machine learning process, the more the data you provide to the system, the more the system can learn from it, and returning all the information you were searching and hence make your search successful. That is why it works so well with big data analytics. Without big data, it cannot work to its optimum level because of the fact that with less data, the system has few examples to learn from. So we can say that big data has a major role in machine learning.

Instead of various advantages of machine learning in analytics of there are various challenges also. Let us discuss them one by one:

Learning from Massive Data:

With the headway of innovation, measure of information we procedure is expanding step by step. In Nov 2017, it was discovered that Google forms approx. 25PB every day, with time, organizations will cross these petabytes of information. The major attribute of data is Volume. So it is a great challenge to process such a huge amount of information. To overcome this challenge, Distributed frameworks with parallel computing should be preferred.

Learning of Different Data Types:

There is a large amount of variety in data nowadays. Variety is also a major attribute of big data. Structured, unstructured and semi-structured are three different types of data that further results in the generation of heterogeneous, non-linear and high-dimensional data. Learning from such a great dataset is a challenge and further results in an increase in the complexity of data. To overcome this challenge, Data Integration should be used.

Learning of Streamed data of high speed:

There are different errands that incorporate culmination of work in a specific timeframe. Speed is additionally one of the significant properties of enormous information. On the off chance that the undertaking isn't finished in a predefined time frame, the aftereffects of handling may turn out to be less profitable or even useless as well. For this, you can take the example of stock market prediction, earthquake prediction, etc. So it is a very necessary and challenging task to process the big data in time. To overcome this challenge, an online learning approach should be used.

Learning of Ambiguous and Incomplete Data:

Previously, the AI calculations were given progressively precise information generally. So the outcomes were additionally exact around then. In any case, these days, there is an equivocalness in the information in light of the fact that the information is created from various sources which are questionable and deficient as well. So, it is a big challenge for machine learning in big data analytics. An example of uncertain data is the data which is generated in wireless networks due to noise, shadowing, fading, etc. To overcome this challenge, Distribution based approach should be used.

Learning of Low-Value Density Data:

The main purpose of machine learning for big data analytics is to extract useful information from a large amount of data for commercial benefits. Value is one of the major attributes of data. To find the significant value from large volumes of data having a low-value density is very challenging. So it is a big challenge for machine learning in big data analytics. To overcome this challenge, Data Mining technologies and knowledge discovery in databases should be used. Universal Informatics is the best training institute for the Machine Learning Training In Indore and other courses.

About the Author

Universal Informatics is an Iso 9001:2008 Certified, process driven IT Services Company, offering a Best It Placement Training Institute In Indore with a wide range of end-to-end services in the IT.

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Author: Jaykumar Jain

Jaykumar Jain

Member since: Jun 10, 2019
Published articles: 8

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