Author: Zen and the Art of Computer Programming
1 Introduction
In recent years, with the development of artificial intelligence, more and more people have begun to pay attention to, learn, use and research this big technology. However, due to the complexity and huge data sets behind machine learning algorithms, people's understanding of them is still not thorough enough, and there is a lack of corresponding tools and methods to help them be better applied to actual scenarios. This article will start from some basic concepts, through a series of theoretical analysis of algorithms and practical cases, to comprehensively introduce all aspects of machine learning. For beginners, you can learn about machine learning overview, algorithm classification, model training, parameter tuning and other basic knowledge from this article; for proficient masters, you can also use this article to check for omissions, systematically summarize relevant knowledge systems, and further improve Skill levels. In addition, this article will also provide some reference materials and answers to frequently asked questions, and strive to give readers a comprehensive understanding of machine learning.
2. Basic concepts
2.1 Concept
Machine Learning (Machine Learning) is a sub-discipline in the field of computer science that explores how to program computers based on data to learn from experience (teach themselves) and improve performance at some task over time. It belongs to the theory of machine learning of statistical learning part. Topics it covers include prediction, decision making, clustering, anomaly detection, association analysis, reinforcement learning, classification and regression, and more. The most important breakthrough in machine learning is that it allows computers to automatically learn from experience to solve a problem or achieve a specific goal. Machine learning is also known as "the main research field of artificial intelligence".
The theory of machine learning consists of algorithms such as supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, genetic algorithm, and Bayesian method. Among them, supervised learning can be divided into supervised learning, weakly supervised learning and unsupervised learning.
2.2 Dataset
Dataset refers to a collection of input and output relationships used to train, test or apply machine learning algorithms. It is an n-dimensional table, each element