Supervised Learning

At the heart of machine learning is "using algorithms to parse data, learn from it, and then make decisions or predictions about something in the world." This means that instead of explicitly writing a program to perform certain tasks, it is better to teach the computer how to develop an algorithm to accomplish the task.

Machine learning is a multi-disciplinary subject that has emerged in the past 20 years and involves many disciplines such as probability theory, statistics, approximation theory, convex analysis, and computational complexity theory.

The simplest definition of machine learning comes from what Berkeley said: Machine learning is a branch of AI that explores ways to make computers more efficient based on experience.

Machine learning is the most advanced aspect of the field of artificial intelligence today, and more beginners have begun to enter this field.

There are many articles on machine learning algorithms that detail the related algorithms, it is still very difficult to make the most appropriate choices.

Machine learning is a combination of art and science. No machine learning algorithm can solve all the problems. There are several factors that can influence your decision to choose a machine learning algorithm.

There are quite a lot of routines for machine learning, but if you have the right path and method, you still have a lot to follow. Here I recommend this blog from SAS's Li Hui, which explains how to choose machine learning.