The world is filled with data. Pictures, music, words, spreadsheets, videos. And this data load is only going to keep increasing. For long, experts have tried to analyze this data for various purposes. But with the ever-increasing data load, there’s a need for a little external support. This is where machine learning comes into play.
Machine learning is the technology that brings in the promise of deriving meaning from all the data that surrounds us.
Traditionally, humans have analyzed data and adapted the technology to the changes in the data pattern. However, the current volume of data surpasses the ability for humans to make sense of it and manually adjust the technology with every change. To solve this issue, we’ve increasingly started turning towards artificial intelligence that not only learns from the current and previously existing data but also helps in predicting future patterns.
So how does machine learning exactly work?
Let’s look at the biggest example of all: Google search. Every time you use google search; you’re using a system that has many machine learning systems at its core. These systems help the search engine in understanding the text of your query and adjusting the results based on your personal interests. Machine learning records and learns for your search pattern. This helps Google to provide you with exactly what you’re looking for. To put it simply, if you search the word ‘java’, it’s these machine learning systems that decide which results to show you first depending on whether you’re a coffee expert or a developer or perhaps both.
Today, machine learning’s immediate applications are already quite wide-ranging. Some of the more well-known uses are image recognition, fraud detection, and recommending systems. However, the capabilities of machine learning can be applied to varied fields, from diabetic retinopathy and skin cancer detection to retail marketing and also transportation, in the form of self-driving vehicles.
With the increasing demand for our technology to be personalized, insightful, and self-correcting just like our ever-evolving interests, the future applications of machine learning are bound to get more diverse.