How to Analyze Big Data Using Machine Learning?
Big Data and Machine Learning have no direct relation to each other. However, the techniques related to Big Data can be utilized in Machine Learning.
Machine Learning is associated with the huge chunks of data. This is the scenario when Big Data plays its role. The volume of data is essential when comprehensive analysis comes to play. There is a plethora of potential relationship between the different data sources, whether it from the live web-based click streams or the back end customer databases. This is extremely demanding for any analyst to check the entire hypothesis and extract the buried values. Hence, a unique tool must be deployed to get it done accurately and seamlessly. To understand the underlying relationship between the machine learning and big data, it is essential to understand the basics. In this article, we will realize the same.
In simple words, machine learning is a process of analyzing data that automates the analytical model construction. It is a part of Artificial Intelligence and is based on the concept that the machines should learn with passing the time and experience to be able to sort various issues. It is a science of developing program and algorithm that is discovered on their own. After the humans build a machine, it will not need to be updated or taught the latest methodologies to perform in a better way. Some of the applications of the Machine Learning are as follows:
- Web search
- Spam filters
- Recommender systems
- Ad placement
- Credit scoring
- Fraud detection
- Computer vision
- Stock trading
- Drug design
In simple terms, it is practically impossible to create machines or systems for every possible search or spam. Hence, it is logical to make the machines intellectual enough to learn in due course. When the later portion of the data mining is automated, it is known as machine learning, which is a self-explanatory term. Machines perform specific tasks which they were not programmed to perform. It is a part of data science and covers not only the algorithmic or statistical aspect but also every aspect of data processing.
Importance of Machine Learning
With time, the need for the machine learning is increasing in a significant way, making it an inevitable part of the data science. It has also made the Bayesian analysis and data mining accessible than it ever has been. Machine learning has been contributing to sorting various issues of growing volumes and variables of data, affordable data storage, and robust and cost-effective computational processing. To curb the accelerating demands and technology, machine learning has effectively able to produce prompt and automated models that can seamlessly analyze the more significant and complex data. It also contributes to delivering an accurate outcome in a minimum span of time even when a considerable data handling is concerned. This also aids the organizations to create a better opportunity to avoid unknown risks and enhance the profitable scopes.
Big Data Analytics
Big Data analytics is an accurate way of understanding a big set of data by identifying the hidden pattern of data, client preferences, market trends, and miscellaneous information to design strategic business models. This helps in excelling in a competitive market immensely. With the help of big data analytics, the data scientists are able to specify the structured as well as unstructured data by deploying the business and analytics intelligence. The big data is a data that is so immense that it usually doesn’t fit the database cluster configuration, slow I/O analysis, and also takes a considerable time to process. Hence, a robust yet straightforward process is needed to handle the data to extract the most from it.
Analyzing Big Data Using Machine Learning
Machine learning is the best way to explore the opportunities hidden in the Big Data, seamlessly and cost-effectively. It helps in significant extraction of the useful value from the different and big data sources with minimum to no human interruption. It is basically data-driven technology and runs at machine scale. It operates in an opposite way to that of the traditional tools. In spite of relying on the humans to teach and update the machines, it thrives on the dynamic sets of data that come across it and adapt the pattern to produce certain outcomes. Hence, if more data is fed into the machine learning system it will become smarter and will analyze the big data with enhanced efficiency within a minimum span of time.
It is clear from the points mentioned above that the big data needs expert and accurate handling to be able to utilize the appropriate values hidden in it. Machine learning can quickly accomplish the activity by learning the pattern of the data and accurately displaying the same within the stipulated time. Therefore, it is suggested to deploy machine learning in the changing sets of data to keep track of the dynamic nature of data.
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