What is machine learning?

Human beings are exposed to data from birth. The eyes, ears, nose, skin, and tongue are continuously gathering various forms of data which the brain translates to sight, sound, smell, touch, and taste. The brain then processes various forms of raw data it receives through sensory organs and translates it to speech, which is used to express opinion about the nature of raw data received.

In today's world, sensors attached to machines are applied to gather data. Data is collected from Internet through various websites and social networking sites. Electronic forms of old manuscripts that have been digitized also add to data sets. Data is also obtained from the Internet through various websites and social networking sites. Data is also gathered from other electronic forms such as old manuscripts that have been digitized. These rich forms of data gathered from multiple sources require processing so that insight can be gained and a more meaningful pattern may be understood.

Machine learning algorithms help to gather data from varied sources, transform rich data sets, and help us to take intelligent action based on the results provided. Machine learning algorithms are designed to be efficient and accurate and to provide general learning to do the following:

• Dealing with large scale problems
• Making accurate predictions
• Handling a variety of different learning problems
• Learning which can be derived and the conditions under which they can be learned

Some of the areas of applications of machine learning algorithms are as follows:

• Price prediction based on sales
• Prediction of molecular response for medicines
• Detecting motor insurance fraud
• Analyzing stock market returns
• Identifying risk ban loans
• Forecasting wind power plant predictions
• Tracking and monitoring the utilization and location of healthcare equipment
• Calculating efficient use of energy
• Understating trends in the growth of transportation in smart cities
• Ore reserve estimations for the mining industry