Research on algorithms of machine learning

Machine learning has endless application possibilities, with many algorithms worth learning in depth. Different algorithms can be flexibly applied to a variety of vertical fields, such as the most common neural network algorithms for face recognition, garbage classification, picture classification, and other application scenarios image recognition and computer vision, the hottest recent natural language processing and recommendation algorithms for different applications are from it. In the field of financial analysis, the decision tree algorithm and its derivative algorithms such as random forest are the mainstream. As well as support vector machines, naive Bayes, K-nearest neighbor algorithms, and so on. From the traditional regression algorithm to the hottest neural network algorithm. This paper discusses the application principle of the algorithm and lists some corresponding applications. Linear regression, decision trees, supervised learning, etc., while some have been replaced by more powerful and flexible algorithms and methods, by studying and understanding these foundational algorithms in depth, neural network models can be better designed and optimized, and a better understanding of how they work can be obtained.