Classical machine learning method

Machine Learning: A Method to Realize Artificial Intelligence

The most basic way of machine learning is to analyze data with algorithms, learn from them, and then make decisions and predictions about events in the real world. Different from traditional hard-coded software programs to solve specific tasks, machine learning is "training" with a large amount of data, and learning how to complete tasks from data through various algorithms.

For a simple example, when we browse online shopping malls, we often have information about product recommendations. This is the goods that the mall determines that you are really interested in and willing to buy according to your previous shopping records and lengthy collection list. This decision-making model can help shopping malls to provide suggestions to customers and encourage product consumption.

Traditional machine learning algorithms include decision tree, clustering, Bayesian classification, support vector machine, EM, Adaboost and so on. This paper will give a general introduction to the commonly used algorithms. There is no code, and there is no complicated theoretical derivation. Knowing what these algorithms are and how to apply them is just a chart.

Decision chart

According to a certain feature classification, each node asks a question, then divides the data into two categories through judgment, and then continues to ask questions. These problems are all learned from the existing data. When new data is put into use, the data can be divided into appropriate leaves according to the problems on this tree.