Author: Zen and the Art of Computer Programming
1 Introduction
With the advancement of science and technology, computer technology has gradually shifted from a simple calculation tool to a tool that can control itself and its surrounding environment. However, as a complex and multi-dimensional data collection, how to extract effective information, make decisions and deal with outliers from the data is still a thorny problem. There is a large amount of data in various complex systems in the real world, which requires various machine learning methods for modeling, analysis and prediction.
In this monograph, the author hopes to help readers understand how to build machine learning with accurate prediction capabilities through the elaboration of basic concepts such as traditional probability theory, fuzzy systems, and machine learning, the introduction of mathematical principles, the implementation of related algorithms, and examples. models, and use practical cases to enhance understanding of the integration of theory and practice. This article is mainly based on the following aspects:
- The first one is probability theory and conditional probability: it must have sufficient theoretical foundation and be able to be fully applied to specific problems;
- The second type, fuzzy system: master the basic theoretical knowledge of fuzzy systems, including associative memory, expert systems, shape similarity, etc.;
- The third type, machine learning: from the simplest linear regression model to deep neural network, understand the principles, training processes and evaluation indicators of various machine learning models;
- The fourth, Python programming language: You can use the Python programming language to put theoretical knowledge into practice and write runnable code;
- The fifth type, case study: use specific cases, based on real scenarios, to show how to use theoretical knowledge such as probability theory, fuzzy systems, machine learning, etc. to build accurate prediction models, and give some difficulties or solutions that may be encountered.
Therefore, the content of this book is not limited to a specific technical field, but involves a comprehensive exposition of knowledge in multiple fields such as probability theory, fuzzy systems, machine learning, Python programming, and practical applications.