Benefits of Machine Learning for Big Data Analytics

Machine learning uses algorithms to find insights that are hidden without having the need to be explicitly programmed. In machine learning, it’s very important to understand the iterative aspect, as models tend to independently adapt when exposed to new data. They iteratively learn from previous data to produce reliable and repeatable decisions and result. It’s nothing new; however, gaining fresh momentum.

With the advancement of new computing technologies, machine learning has greatly evolved from that of past. Recently, machine learning algorithms are able to apply complex mathematical calculations to big data quickly on a regular basis, which was not there for a long time. Some of the most familiar examples are mentioned below.

With the advancement in data mining and analysis, there has been an increase in interest towards machine learning. Data have increased in variety and volume, computation has become powerful and economical, and data storage has become affordable. Most of the daily activities are powered by machine learning algorithm, including New pricing model, Credit scoring, Next-best offers, web search result, Email spam filtering, and more.

Some of the most popular and widely used machines learning methods include supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. However, supervised learning accounts for 70 percent of the machine learning and unsupervised learning accounts for around 10-20%.





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