机器学习
人工智能
计算机科学
聚类分析
朴素贝叶斯分类器
决策树
Python(编程语言)
无监督学习
支持向量机
操作系统
作者
Ravinder Ahuja,Aakarsha Chug,Shaurya Gupta,Pratyush Ahuja,Shruti Kohli
出处
期刊:Studies in computational intelligence
日期:2019-09-03
卷期号:: 225-248
被引量:35
标识
DOI:10.1007/978-3-030-28553-1_11
摘要
In order to minimize human effort and increase efficiency, we use machines. But nowadays, advancements have been done to such an extent that machines can learn from experience and make decisions by itself substituting humans. Machine learning is basically a subfield of Artificial Intelligence, which is based on the principal of a machine being able to analyze patterns, learn from data and thereby make decisions itself with minimal or none explicit assistance. This is an introductory chapter to machine learning containing supervised, unsupervised, semi-supervised, and reinforcement algorithms and applications of machine learning. This chapter covered four classification techniques (Logistic Regression, Decision Tree, K-Nearest Neighbors, and Naive Bayes) and K means, and Hierarchical clustering algorithms considering two well-known datasets (Iris and tennis) using Python.
科研通智能强力驱动
Strongly Powered by AbleSci AI