Merriam Webster defines prediction as “predicting the future or foretelling it in advance” and is based on observation, experience, scientific reasoning, and observation. According to Cambridge dictionary prediction can be defined as an assertion about the future. A pattern of evidence is used to predict the future. This is done based upon prior knowledge and evidence. Prediction refers to the conclusion of statistical inference. While prediction in statistics refers only to the conclusion of statistical inference, science is a rigorous, often quantitative analysis that uses past and present data.
Predictions are used in every aspect of our lives: medical researches and engineering, forecasting, finance, marketing, game, technology, communication and construction. Predictions are a big part of our daily lives. Amazon, Jumia or Konga will predict what products you’ll like to purchase each time you shop. Netflix and other movie streaming sites can help you predict what movie will be most popular. Google uses this technology to predict your response to emails. Match.com and the other dating sites can even predict your chances of falling in love. Prediction is possible in households where children can predict when their father will return home and wives can predict the movements of their husbands. A lecturer can predict the student’s possible grade, based on his grade and how serious he is. These predictions are so automatic that we no longer notice them. Machine Learning was used to assist in these predictions. Machine Learning, a modern application of artificial Intelligence, is based on the belief that computers should just be able give data to machines and allow them to learn for themselves. Machine learning can process huge amounts of data at high speed, whereas humans are unable to comprehend it. Machine Learning is an old technology, but the modern computers are capable of running it. It was not possible to run the program on any computer in the 20th-century. Only a handful of computers can do it today. The availability of large data increases machine learning’s effectiveness, as machine learning algorithms require large amounts data in order to function efficiently and accurately. Machine learning can be done in three ways: unsupervised, supervised, and reinforced learning. You train an algorithm using data that has the answer in supervised learning. You can train an algorithm to identify friends by name. This is what you call unsupervised learning. Reinforced Learning is when you set a goal for a machine, and then expect it to try and succeed. Machine learning applications are few and far between. These include self-driving Google vehicles, online recommendation systems such as Amazon and Netflix and the ability to see what customers have to say about you via Twitter. Prediction is so common that it can be applied to nearly every aspect of our lives. Predictions include predicting weather conditions and the likelihood of rain, or predicting how a football match will end. Prediction gives us control. Knowing what is going to happen in the future helps us plan ahead and makes it easier to control things. Prediction guides and helps us make the right decisions to reach our goals and avoid any discomfort. Prediction helps to guide the steps needed to reach a specific goal. Machine learning makes prediction more accurate and quicker. Machine Learning can process large amounts of data that are impossible to process, or that would take years for humans to process. This allows it to predict with greater accuracy in a shorter time period than human prediction.
