Machine learning is a branch of computer science, a field of artificial intelligence. It is a data analysis method that further helps automate analytical model building. Instead, as the word implies, it provides machines (computer systems) with the ability to learn from data, without outside help to make decisions with minimal human intervention. With the development of new technologies, machine learning has changed a lot over the past few years.<\/p>\n

Let’s discuss what is big data?<\/p>\n

Big data means a lot of information and analytics means analyzing a large amount of data to filter the information. It is not possible for a human being to do this task efficiently within a fixed period of time. So this is the point where machine learning for big data analytics comes into play. To take an example, let’s say you are the owner of the company and you need to gather a large amount of information, which is very difficult on its own. Then you start to find a guide that will help you in your work or make decisions faster. Here you realize that you are dealing with an enormous amount of information. Your analytics need a little help to make the search work. In the process of machine learning, the more data you provide to the system, the more the system can learn from it, return all the information you were looking for, and thus make your search successful. This is why it works so well with big data analytics. Without big data, it cannot be optimized due to 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.<\/p>\n

Instead of the various advantages of machine learning in analytics, there are also different challenges. Let’s discuss them one by one:<\/p>\n

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  Learning from big data: <\/b>With the advancement of technology, the amount of data we process is increasing day by day. In November 2017, it was found that Google is processing . 25 petabytes a day, over time companies will cross petabytes of data. The main characteristic of the data is the size. So processing such a huge amount of information is quite a challenge. To overcome this challenge, distributed frameworks with parallel computing should be preferred.<\/p>\n<\/li>\n

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  Learn the different types of data: <\/b>There is a great deal of diversity in data nowadays. Diversity is also a key feature of big data. Structured, unstructured, and semi-structured are three different types of data that lead to the generation of heterogeneous, non-linear, and high-dimensional data. Learning from such a large dataset is challenging and leads to increased data complexity. To overcome this challenge, data integration must be used.<\/p>\n<\/li>\n

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  Learn streaming data at high speed: <\/b>There are many tasks which include completion of work in given time period. Speed \u200b\u200bis also one of the main features of big data. If the task is not completed in a specified period of time, the processing results may become less valuable or even worthless as well. For this, you can take the example of stock market forecasts, earthquake prediction, etc. So it is very necessary and difficult to process big data in time. To overcome this challenge, an online learning approach must be used.<\/p>\n<\/li>\n

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  Learn fuzzy and incomplete data: <\/b>Previously, machine learning algorithms were fed relatively more accurate data. So the results were also accurate at that time. But nowadays there is ambiguity in the data because the data is generated from different sources which are not confirmed and also not complete. Therefore, this is a major challenge for machine learning in big data analytics. Examples of uncertain data is the data that is generated in wireless networks due to noise, shadowing, fading, etc. To overcome this challenge, a distribution-based approach must be used.<\/p>\n<\/li>\n

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  Learn low value density data: <\/b>The main purpose of machine learning for big data analytics is to extract useful information from a large amount of data to achieve business benefits. Value is one of the main attributes of data. It is very difficult to find the important value from large amounts of low value-density data. So it is a big challenge for machine learning in big data analytics. To overcome this challenge, data mining and knowledge discovery techniques must be used in databases.<\/p>\n<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"

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