Artificial Intelligence is starting to dominate the world. It is clear that computers perform more and more efficiently every day with minimal human intervention; however, what is the source of the computers' high level of intelligence? In the 20th century, a marvellous idea emerged among scientists – that the systems can learn from the data, identify patterns and make decisions on their own, and so based on this idea, a new branch of AI – Machine Learning – was developed.

How was Machine Learning Invented?

Even though nowadays, Machine Learning is a prevalent practice in pretty much all tech companies, it was not the same in the past. Firstly, Machine Learning was born from pattern recognition and the theory that computers can learn how to complete specific tasks without being programmed. Hence, they discovered the adapting ability of a computer, which is one of the most important and beneficial tools for developing AI even today. Moreover, the decisions independently produced by the machines were found to be incredibly reliable and repeatable. It is not surprising that this revolutionary science immediately gained momentum.

Many Machine Learning algorithms have been around for quite a long time, but their sophistication and improvement to the best of their potential is the task of our era. One recent development, for example, would be the ability to automatically apply complex mathematical calculations to massive data in the most time-efficient way possible. 

Practical Applications of Machine Learning

So, we learned that Machine Learning is the computer's ability to learn on its own, but why is it so important in our everyday lives? Most industries have well exploited this subset of AI in many work sectors. For example: in Financial Services, Machine Learning is mainly used to identify important insights and frauds in the data; Government Agencies use it for analyzing sensor data and for minimizing identity theft; Health Care uses wearable devices and sensors to assess a patient's health in real-time; Retail websites use Machine Learning to generate recommendations for you and many more. Remember, these are only a few examples of the massive practical applications provided by Machine Learning systems. 

Building a Good Machine Learning System 

Now, as we've assessed the usefulness and practical applications, you might be wondering what it takes to create a good Machine Learning system. To break it down into several points, it would be:

• Data preparation capabilities;
• Advanced algorithms;
• Automation and iterative processes;
• Scalability;
• Ensemble modelling. 

All the features mentioned above define a sophisticated Machine Learning system, which you probably already believe is one of the most important technical tools we obtain. Arthur C. Clarke once said, `Any sufficiently advanced technology is indistinguishable from magic`. This statement can't be more relatable to anything else than the Machine Learning revolution of the 21st century.