店 机器学习实战营 从理论到实战的探索之旅 人工智能深度学习教程 数据处理 数据预处理 神经网络智能识别教材书 谢雪葵

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，*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。

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
Python 介绍···········································································.14
2.1.1 Python 的安装 ·······························································.14
2.1.2 Python 基础语法 ····························································.19
2.1.3 Python 其他特性 ····························································.24
2.1.4 Python 简单实战案例(猜字游戏) ····································.31
2.1.5 Python *级实战案例(网络爬虫) ····································.35
数据科学库···········································································.38
2.2.1 NumPy ········································································.38
2.2.2 Pandas ·········································································.45
2.2.3 数据科学库案例(电商网站) ··········································.54
机器学习库···········································································.55
2.3.1 Scikit-Learn···································································.55
2.3.2 TensorFlow ···································································.60
2.3.3 Keras···········································································.64
2.3.4 机器学习库案例(预测糖尿病) ·······································.67
数据预处理·············································································70
数据导入 ··············································································.70
数据清洗 ··············································································.71
特征工程 ··············································································.73
3.3.1 特征选择······································································.73
3.3.2 特征转换······································································.75
3.3.3 特征缩放······································································.77
数据分割 ··············································································.78
3.4.1 训练集·········································································.78
3.4.2 测试集·········································································.79
3.4.3 验证集·········································································.80
案例分析:银行客户数据·························································.80
机器学习模型的构建与评估························································84
监督学习实战········································································.84
4.1.1 线性回归······································································.84
4.1.2 逻辑回归······································································.86
4.1.3 决策树·········································································.88
4.1.4 随机森林······································································.90
无监督学习实战·····································································.91
4.2.1 K-means ·······································································.92
4.2.2 主成分分析···································································.93
深度学习实战········································································.95
4.3.1 神经网络······································································.95
4.3.2 卷积神经网络································································.98
4.3.3 循环神经网络································································102
模型评估与选择 ·····································································105
案例分析:客户流失预测 ·························································107
第5章 5.1
机器学习项目实战···································································111
项目一:房价预测 ·································································.111
5.1.1 数据获取与理解·····························································112
5.1.2 数据预处理···································································116
5.1.3 特征工程······································································120
5.1.4 模型构建与训练·····························································123
5.1.5 模型评估与优化·····························································125
5.1.6 结果解释······································································128
项目二:图像识别 ··································································130
5.2.1 数据获取与理解·····························································131
5.2.2 数据预处理···································································134
5.2.3 特征工程······································································136
5.2.4 模型构建与训练·····························································138
5.2.5 模型评估与优化·····························································140
5.2.6 结果解释······································································143
项目三:自然语言处理 ····························································144
5.3.1 数据获取与理解·····························································144
5.3.2 数据预处理···································································147
5.3.3 特征工程······································································148
5.3.4 模型构建与训练·····························································149
5.3.5 模型评估与优化·····························································151
5.3.6 结果解释······································································157
项目四:新闻主题分类 ····························································157
5.4.1 数据获取与理解·····························································158
5.4.2 数据预处理···································································161
5.4.3 特征工程······································································164
5.4.4 模型构建与训练·····························································166
5.4.5 模型评估与优化·····························································168
5.4.6 结果解释······································································171
项目五:信用卡欺诈检测 ·························································172
5.5.1 数据获取与理解·····························································173
5.5.2 数据预处理···································································176
第5章 5.1
机器学习项目实战···································································111
项目一:房价预测 ·································································.111
5.1.1 数据获取与理解·····························································112
5.1.2 数据预处理···································································116
5.1.3 特征工程······································································120
5.1.4 模型构建与训练·····························································123
5.1.5 模型评估与优化·····························································125
5.1.6 结果解释······································································128
项目二:图像识别 ··································································130
5.2.1 数据获取与理解·····························································131
5.2.2 数据预处理···································································134
5.2.3 特征工程······································································136
5.2.4 模型构建与训练·····························································138
5.2.5 模型评估与优化·····························································140
5.2.6 结果解释······································································143
项目三:自然语言处理 ····························································144
5.3.1 数据获取与理解·····························································144
5.3.2 数据预处理···································································147
5.3.3 特征工程······································································148
5.3.4 模型构建与训练·····························································149
5.3.5 模型评估与优化·····························································151
5.3.6 结果解释······································································157
项目四:新闻主题分类 ····························································157
5.4.1 数据获取与理解·····························································158
5.4.2 数据预处理···································································161
5.4.3 特征工程······································································164
5.4.4 模型构建与训练·····························································166
5.4.5 模型评估与优化·····························································168
5.4.6 结果解释······································································171
项目五:信用卡欺诈检测 ·························································172
5.5.1 数据获取与理解·····························································173
5.5.2 数据预处理···································································176

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。

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，*后通过若干典型案例加深 读者对机器学习的理解。

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，*后通过若干典型案例加深 读者对机器学习的理解。

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