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.
- The essence of machine learning can be seen in Google’s self-driving car.
- Machine learning application can be seen in everyday life like recommendation offers from Amazon, Netflix online.
- It can be mixed with linguistic rule creation like knowing the customers what they are saying on social media like Twitter.
- Machine learning can be used in fraud detection.
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%.
- Supervised learning algorithms are used where the desired output is known. The algorithm is provided with input sets and corresponding output sets. The algorithm analyzes by comparing the actual output with the correct outputs.
- Unsupervised learning is used against data with no historical labels. The algorithm should figure what is being shown without knowing the ‘Right Answer’.
- Semi-supervised learning uses both labeled and unlabeled data with methods like classification, regression, and prediction.
- Reinforcement learning uses algorithms that discover through trial and error method. It is often used in gaming, navigation, and robotics.