How Are Big Data and Machine Learning Related?

Machine learning (ML) in short algorithms which can learn from data without relying on rules-based programming. Big data is the type of data that may be supplied into the analytical system so that a Machine learning (ML) model could learn to improve the accuracy of its predictions. Big Data and Machine Learning have a weak relation. We can only apply Machine Learning on Big Data or Big Data can only be handled via Machine Learning paradigms. So, essentially Big Data and Machine Learning are not directly related but they may co-share tools which are of practical importance. They are fields. Machine Learning and Big Data as such have no direct relation. Although it may be said that Big Data Techniques can be used in Machine Learning.

In the fields like Pattern recognition, data mining, knowledge discovery, both machine learning and statistics are coming closer.

The common goal is learning from data; which means both of the fields focus on drawing insights or knowledge from data. However, both methods are affected by their inherent differences. Statistics is a subfield of mathematics, machine learning is of computer science (and artificial intelligence). Machine learning involves no prior assumptions of underlying relationships between the variables. Machine learning algorithm processes data and discovers patterns. We can then use these patterns on a new data set. Machine learning is commonly used in case of high-dimensional data sets. More data we have, the more accurate we are at predictions. Predicting with statistics means we need to know precisely how the data was collected, and the underlying distribution.

In a simple Map-Reduce scenario when we have to find the frequency of words in a very large corpus of data there is no machine learning in counting the total occurrence of words in a data-set. But it is Big Data work. Big Data is the art of working with large amount of data. Machine learning could be carried out on a smaller set of data, but larger the data better will be the predictions.

The goal of big data analytics is to help the companies make more informed business decisions by enabling the data scientist, predictive modelers and other analytics professionals to analyze large volumes of transaction data, as well as other forms of data that may be untapped by conventional business intelligence(BI) programs. Machine Learning is the science of creating algorithms and program which learn on their own. Once designed, they do not need a human to become better. Big Data Analytics is studying large datasets to identify hidden patterns, market trends, consumer preferences and other valuable information helping organizations to form strategic business decisions. With Big data analytics, data scientists and other analytics professionals can examine huge amounts of structured data as well as the untapped data by deploying analytics and business intelligence.

Once again, they are actually not related. Machine Learning, Deep Learning are continuation original idea of artificial intelligence of 1960s. Learning is different, yet they has been dependent on Big Data in many use-cases.