The term Machine Learning was coined by Arthur Samuel (1959), an American pioneer in the field of computer gaming and artificial intelligence, and stated that “it gives computers the ability to learn without being explicitly programmed.”
Since the internet became popular, the amount of data generated worldwide has been immeasurable. According to Forbes, Americans use 4,416,720 GB of internet data, including 188,000,000 emails, 18,100,000 texts, and 4,497,420 Google searches every minute.
With so much data available and computational processing getting cheaper and much more powerful, something had to be done with this data. That’s when machine learning was born, and it became a way for humans to understand critical aspects of the vast amount of data.
Most top-tier companies build machine learning models to identify profitable opportunities and avoid risks.
These machine learning models improve decision-making by doing tasks such as predicting whether a stock will go up or down, forecasting company sales, etc. They also help uncover patterns and trends in datasets and solve super-complex problems.