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机器学习实战营：从理论到实战的探索之旅 
定 价：68 元
9 7 4 8 7 7 8 1 1 2 5 1 4 读者对象: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。 
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。
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
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 高级实战案例(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
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