作者：谢雪葵
页数：216
出版社：电子工业出版社
出版日期：2024
ISBN：9787121478154
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内容简介
本书是一本机器学习实用指南，提供从基础知识到进阶技能的全面学习路径。本书以浅显 易懂的方式介绍了机器学习的基本概念和主要类型，并详细介绍使用 Python 及常见的库进行数 据处理和机器学习的实操。此外，介绍了数据预处理的详细过程，最后通过若干典型案例加深 读者对机器学习的理解。本书适合对机器学习感兴趣的初学者，也可作为软件开发人员、数据分析师、学术研究人员的参考书籍。
作者简介
谢雪葵，毕业于北邮软件学院计算机科学系软件工程专业。在校期间，多次获得专业一、二等奖学金，并成功带领团队进行了校园APP的研发工作。阿诚网络的创始人，该公司专注于为企业提供大数据相关服务。主要业务包括为企业提供大数据技术支持和降低成本、提高效率的解决方案，同时也提供基于机器学习的预测模型和智能决策支持。在过去的多年里，积累了丰富的企业级大数据项目实战经验，并负责大型银行和互联网公司的大数据项目开发和性能优化工作，其中包括使用机器学习技术进行风险评估、用户行为分析和产品推荐等。
目录
目录
机器学习入门············································································1
机器学习简介 ···········································································1
1.1.1 什么是机器学习································································1
1.1.2 机器学习的前景································································2
机器学习的主要类型 ··································································3
1.2.1 监督学习·········································································4
1.2.2 无监督学习······································································5
1.2.3 半监督学习······································································7
1.2.4 强化学习·········································································8
1.2.5 监督学习案例································································.10
选择正确的算法·····································································.12
机器学习工具和环境·································································14
Pyth
机器学习入门············································································1
机器学习简介 ···········································································1
1.1.1 什么是机器学习································································1
1.1.2 机器学习的前景································································2
机器学习的主要类型 ··································································3
1.2.1 监督学习·········································································4
1.2.2 无监督学习······································································5
1.2.3 半监督学习······································································7
1.2.4 强化学习·········································································8
1.2.5 监督学习案例································································.10
选择正确的算法·····································································.12
机器学习工具和环境·································································14
Pyth
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