Machine learning is a branch of computer science, which is an area of artificial intelligence. This is a data analysis method that helps to automate the creation of analytical model. Alternatively, as the word indicates, it provides machines (computer systems) with the ability to learn from data, to make decisions with minimal human intervention without external help. With the development of new technologies, machine learning has undergone a lot of change in the past few years.
Let’s discuss what is Big Data?
Big data means a lot of information and analytics means a large amount of data analysis to filter information. A human cannot do this task efficiently within a given time limit. So here’s the point where machine learning for big data analytics comes in the game. We take an example, assume that you are the owner of the company and there is a need to collect large amounts of information, which is very difficult in itself. Then you start to find a clue that will help your business or make a quick decision. Here you realize that you are working with immense information. To make search successful, your analysis needs a bit of help. In the process of machine learning, the more data you provide to the system, the more systems can learn from it, and you can return all the information you are looking for and therefore make your search successful. That’s why it works so well with large data analytics. Without large data, it can not work at its optimum level due to the fact that with less data, the system has some examples for learning. So we can say that major data is major role in machine learning.
Instead of various benefits in the analysis of the machine there are various challenges. Let’s discuss them:
Learning from large-scale data: With the advancement of technology, the amount of data we process is increasing day by day. In November 2017, it was found that Google almost estimates. 25PB per day, over time, companies will exceed these petabytes of data. The key feature of the data is the volume. Therefore it is a big challenge to process such a large amount of information. In order to overcome this challenge, the distributed structure with parallel computing should be preferred.
Learning different data types: Nowadays there is a large amount of diversity in the data. Diversity is also a major feature of large data. There are structured, unstructed, and semi-structured three different types of data that further result in generation of odd, non-linear and high-dimensional data. Learning from such a great dataset is a challenge and increases the complexity of the data. To overcome this kind of challenge, data integration should be used.
Learning about high speed streamed data: There are several tasks that include completion of work at a given time. Velocity is also one of the major characteristics of large data. If the work is not completed in the specified period, the processing results can be less valuable or too useless. For this, you can take an example of predicting stock market, forecasting earthquake etc. Therefore, it is a very necessary and challenging task to process large data in time. In order to overcome this challenge, online learning approach should be used.
Learning of ambiguous and incomplete data: First, the machine learning algorithm was given more accurate data. Then the results were also accurate at that time. But nowadays, the data is ambiguous because the data is generated from different sources which are uncertain and also incomplete. Therefore, this is a big challenge for learning machine in large data analytics. An example of uncertain data is the data that is generated in the wireless network due to noise, shading, disappearing etc. To overcome this kind of challenge, distribution based approach should be used.