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. Alternatively, as the word indicates, it provides the machines (computer systems) with the capability to learn from the data, without external help to make decisions with minimum human interference. With the evolution of new technologies, machine learning has changed a lot over the past few years.
What is Big Data?
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. Then you start to find a clue that will help you in your business or make decisions faster. Here you realize that you're dealing with immense information. Your analytics need a little help to make search successful. In the machine learning process, more the data you provide to the system, 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, there are various challenges also. Let us discuss them one by one:
Learning from Massive Data: With the advancement of technology, the amount of data we process is increasing day by day. 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 various tasks that include completion of work in a certain period of time. Velocity is also one of the major attributes of big data. If the task is not completed in a specified period of time, the results of processing may become less valuable or even worthless too. For this, you can take the example of stock market prediction, earthquake prediction, etc. So it is 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 machine learning algorithms were provided with more accurate data relatively. So the results were also accurate at that time. But nowadays, there is an ambiguity in the data because the data is generated from different sources which are uncertain and incomplete too. So, it is a big challenge for machine learning in big data analytics. 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.