Machine Learning, an interesting area of Artificial Intelligence, is all around us in today’s society. Applications of Machine Learning, like Facebook’s suggestion of articles in your feed, bring out the potential of data in a new way.
But it seems like the term Machine Learning is used so often but explained rather infrequently. Today, we’ll look at what Machine Learning is and some applications of Machine Learning!
What is Machine Learning?
Machine Learning is a fundamental sub-area of Artificial Intelligence. This sub-area focuses on the creation of computer programs that can access data and perform tasks automatically through predictions and detections, allowing computers to learn and improve over time.
Like people, Machine Learning applications learn through experience (well data) rather than being programmed. These applications learn, grow, alter, and evolve on their own when they are exposed to fresh data. You enhance the provided results as you feed the computer additional data, enabling the algorithms that lead it to “learn.” To put it another way, Machine Learning allows computers to locate useful information without having to be instructed where to search. Instead, they use algorithms that learn from data in an iterative process to do this.
The ability to automate the application of complex mathematical calculations to Big Data has been gaining momentum over the last several years. Let’s take a look at some of it’s applications.
Applications of Machine Learning
We see applications of Machine Learning in our day-to-day life everywhere around us. Web search results, real-time advertisements on web pages and mobile devices, email spam filtering, network intrusion detection, and pattern and picture recognition are examples of typical results from Machine Learning applications that we either see or don’t see on a regular basis. All of these are unintended consequences of utilizing Machine Learning to examine large amounts of data.
Machine Learning and its future
Data analysis has traditionally relied on trial and error, which is hard to do when data sets are big and diverse. Machine Learning offers innovative approaches to evaluating large amounts of data. Machine Learning can generate correct results and save time by building quick and efficient algorithms and data-driven models for real-time data processing.
The message here is clear: Machine Learning is the future and the future is now.