Merriam Webster Dictionary defines prediction as the art or science of anticipating what will happen, especially using observation, experience and/or scientific reasoning. According to the Cambridge dictionary, prediction refers to a prediction about what will happen in future. A pattern of evidence is used to predict the future. It is based upon previous knowledge and evidences. Prediction in statistics is a conclusion made based upon statistical inference. In science, it’s a quantitative and rigorous analysis of past data and occurrences to predict what will occur under certain circumstances.
Predictions have been used in almost every aspect of human life: in medicine, engineering, geography and forecasting, finance, market, sports, games, technology and communication, and so forth. Predictions are a big part of our daily lives. Amazon, Jumia and Konga can predict what you will like to purchase every time you shop. Netflix and other movie websites can predict what movie you will like to see. Google uses this technology to predict your response to emails. Match.com is one of the dating sites that can predict your future partner. There are many examples of prediction: children predict when their father is home, and wives predict the movements for 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 is an emerging application of artificial intelligent. It is based on the belief that computers should have access to all data so they can learn for themselves. Machine learning is capable of processing large data sets that are difficult to understand and can handle them at an incredible speed. Machine Learning has existed since the 20thcentury, but its popularity is now due to the availability of powerful computers capable of running it. In the 20th-century, there was no computer capable of running it. Only a handful of computers can still run it efficiently and well. Machine learning also benefits from large data because it allows for the training of algorithms with high accuracy and efficiency. 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. When you ask a machine for help identifying your friends, it will first identify the data. This is what you call unsupervised learning. Reinforced learning is when you give a machine goals and expect it to use trial and error to reach them. A few of the most well-known examples of machine learning are: Google’s self-driving car, Amazon’s online recommendation service, and Amazon’s online recommendations. You can also see what customers have to say about you via Twitter. Machine learning can also be used for fraud detection, speech recognition, and image recognition. Prediction is so common that it can be applied to nearly every aspect of our lives. Predictions can be made based on weather conditions. 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 can help us avoid discomfort and guide our decisions in order to reach our goal. If we know the outcome of each step before we start, this helps us to plan the steps that will lead to achieving a 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 humans.
