Machine learning is a branch of computer science, a field of artificial intelligence. It is a method of data analysis helps further automate the construction of analytical models. Alternatively, as the word indicates, it gives machines (computer systems) the ability to learn from data, without external help to make decisions with minimal human interference. With the evolution of new technologies, machine learning has changed a lot in recent years.
Let’s discuss what is Big Data?
Big data means too much information and analysis means analyzing a large amount of data to filter the information. A human cannot do this task efficiently within a time limit. So, this is where machine learning for big data analysis comes into play. Take an example, suppose you are the owner of a company and need to collect a lot of information, which is very difficult on its own. Then, start finding a clue that will help you in your business or make decisions faster.
Here you realize that you are dealing with immense information. Your analysis needs some help to make the search successful. In the machine learning process, the more you provide data to the system, the more the system can learn. And obviously it would return all the information any person is looking for and since then make his search successful. That’s why it works so well with big data analysis. Without large data, it cannot function at its optimum level. In addition, with less data, the system has few examples to learn. Then, we can say that big data has an important role in machine learning.
Instead of several advantages of machine learning in analysis, there are also several challenges. Let’s discuss them one by one:
Learning from massive data:
With the advancement of technology, the amount of data we process increases day by day. In November 2020, Google processed approx. 25PB per day, over time, companies will cross these petabytes of data. The main attribute of the data is volume.
Therefore, it is a great challenge to process such a large amount of information. To overcome this challenge, distributed frames with parallel computing should be preferred. Noted that, nowadays FX broker comparison plays an important role in the Forex trading business.
Learning different types of data:
There is a great variety of data these days. Variety is also an important big data attribute. Structured, unstructured and semi-structured are three different types of data that result in the generation of heterogeneous, non-linear and high-dimension data. Learning from such a good data set is a challenge and the additional results increase the complexity of the data. To overcome this challenge, data integration must be used.
Learning high speed transmitted data:
There are several tasks that include completion of work in a certain period of time. Speed is also one of the main attributes of big data. If the task is not completed within a specific period of time, the processing results may be less valuable or even useless. For this reason, you can take the example of stock market prediction, earthquake prediction, etc. Therefore, it is a very necessary and challenging task to process big data on time. To overcome this challenge, the online learning approach must be used.
Learning ambiguous and incomplete data:
Previously, machine learning algorithms were provided with greater accuracy in the data. So, the results were also accurate at that time. But today, there is an ambiguity in the data. Because the data is generated from different sources that are also uncertain and incomplete. So, it is a great challenge for machine learning in big data analysis. Examples of uncertain data are the data that is generated in wireless networks due to noise, shadow, fading and etc. To overcome the challenge, the distribution-based approach must be used.
Learning low-density data:
The main purpose of machine learning for big data analysis is to extract useful information from a large amount of data used for commercial benefits. Value is one of the key attributes of the data. Finding the significant value of large volumes of data with a low-value density is very difficult. Therefore, it is a great challenge for machine learning in big data analysis. To overcome the challenge, Data Mining technologies and knowledge discovery in databases must be used.
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