Machine learning is a popular topic in the computer science area, but it can be used in many other different areas. For example, it can predict the probability that it might rain or classify the species of bird. Machine learning is a powerful technic which will be used in our future life. Machine learning is a good tool for us to insight data and asks the computer to help us deeply understand the data meaning . According to Mitchell, the definition of the machine learning is shown as follow: “a computer program is said to learn from experience E, with respect to some task T, and some performance measure P, if its performance on T as measured by P improves with experience E.”. Three features in this definition are the class of the tasks, the measure of performance to be improved and the source of experience . The experience E is like where the machine learns from. In general, the machine learning algorithm learns from the dataset. The task T is what we want the machine doing at the final or after training. The possible P is the possibility of machine completing the task or the accuracy of result from the algorithm. In the training process, a computer is more patient than people, so it can repeat analysis same data many times and may find a better choice than people thinking. There are two general classifications of the machine learning. One is supervised learning and another is unsupervised learning. The difference between these two types of machine learning algorithm is whether people teach the computer how to do something or tell the machine which choice is right. In other words, when the data is labelled, it is a supervised learning. In unsupervised learning, there is no labelled data and the computer does not know what is right and it will find the answer by itself . The detail of supervised and unsupervised learning will be discussed in a series of later article. Moreover, how to choose and improve the algorithm is another important thing when design the machine learning system.
P. Harrington, Machine learning in action. Shelter Island, N.Y.: Manning Publications Co., 2012.
T. Mitchell, Machine learning. Singapore: McGraw-Hill, 1997, pp. 1-4.