机器学习技术任务驱动式教程
定价：￥52.00
作者： 艾旭升
出版时间：2025-08
出版社：电子工业出版社
- 电子工业出版社
- 9787121490873
- 1-2
- 540615
- 68260282-6
- 平塑
- 16开
- 2025-08
- 416
- 260
- 电子与信息大类
- 计算机类
- 高职
内容简介
本教材内容包括了解机器学习、机器学习开发环境安装及使用，以及 8 个大类的预测任务（涵盖监督学习、无监督学习、集成学习、深度学习等知识点）。在内容的选取上，本教材突出数据的多样性、方法的代表性和继承性，通过丰富的机器学习任务深入浅出地介绍机器学习技术在现实生活中的应用和实践。本教材共有 10 个模块，第 1～2 个模块是实施机器学习任务前的概念介绍和环境准备，第 3～8 个模块结合案例介绍 K-最近邻、线性回归和逻辑回归、决策树、贝叶斯模型、支持向量机、K-平均值等机器学习算法及模型，第 9、10 个模块分别介绍了结合多分类器的集成学习方法和源自神经网络模型的深度学习算法。从第 3 个模块开始，每个模块至少包含 3 个案例，涵盖数据清洗、数据转换、模型训练、模型评估、结果可视化等大数据和人工智能技术。本教材提供丰富多样的机器学习任务，借鉴企业项目开发的工作流程，采用 JupyterLab 融合注释、代码和运行结果，图文并茂地介绍机器学习任务的编码过程。在编码过程中，涉及机器学习概念、公式及注意事项的详细讲解。本教材适用于高职高专院校大数据和人工智能技术应用专业开展任务驱动式教学，也可作为机器学习初学者的启蒙资料。
目录
模块1 了解机器学习 ···············································································1
1.1 机器学习简介 ···················································································2
1.2 机器学习发展史 ··············································································· 2
1.3 机器学习应用领域 ············································································ 3
1.3.1 金融业 ························································································ 3
1.3.2 保险业 ························································································ 3
1.3.3 制造业 ························································································ 4
1.3.4 零售业 ························································································ 4
1.3.5 医疗保健 ····················································································· 4
1.3.6 司法 ··························································································· 4
1.3.7 工程与科学 ·················································································· 4
1.4 机器学习常用方法 ··········································································· 4
1.4.1 K-最近邻 ····················································································. 5
1.4.2 回归 ··························································································· 5
1.4.3 决策树 ························································································ 6
1.4.4 贝叶斯模型 ·················································································· 6
1.4.5 支持向量机 ·················································································· 6
1.4.6 聚类 ··························································································· 6
1.4.7 集成学习 ····················································································· 7
1.4.8 深度学习 ····················································································· 7
1.5 sklearn 机器学习库 ·········································································· 7
小结 ···································································································8
习题 ···································································································8
模块2 机器学习开发环境安装及使用 ·························································9
2.1 机器学习开发环境安装 ·········································································· 10
2.1.1 安装 Anaconda ············································································· 10
2.1.2 安装 JupyterLab ··········································································· 15
2.1.3 安装机器学习库 ··········································································· 18
2.2 基于JupyterLab 环境编程 ······································································· 22
2.2.1 进入 JupyterLab 环境····································································· 22
2.2.2 编写程序 ···················································································· 23
2.2.3 保存及运行程序 ··········································································· 25
2.3 JupyterLab 程序调试 ·············································································· 26
2.3.1 打印变量值 ················································································· 26
2.3.2 JupyterLab Debugger 调试 ······························································· 27
2.4 Markdown 目录制作 ·············································································· 30
小结 ········································································································· 32
习题 ········································································································· 32
模块3 基于K-最近邻的分类预测 ····················
1.1 机器学习简介 ···················································································2
1.2 机器学习发展史 ··············································································· 2
1.3 机器学习应用领域 ············································································ 3
1.3.1 金融业 ························································································ 3
1.3.2 保险业 ························································································ 3
1.3.3 制造业 ························································································ 4
1.3.4 零售业 ························································································ 4
1.3.5 医疗保健 ····················································································· 4
1.3.6 司法 ··························································································· 4
1.3.7 工程与科学 ·················································································· 4
1.4 机器学习常用方法 ··········································································· 4
1.4.1 K-最近邻 ····················································································. 5
1.4.2 回归 ··························································································· 5
1.4.3 决策树 ························································································ 6
1.4.4 贝叶斯模型 ·················································································· 6
1.4.5 支持向量机 ·················································································· 6
1.4.6 聚类 ··························································································· 6
1.4.7 集成学习 ····················································································· 7
1.4.8 深度学习 ····················································································· 7
1.5 sklearn 机器学习库 ·········································································· 7
小结 ···································································································8
习题 ···································································································8
模块2 机器学习开发环境安装及使用 ·························································9
2.1 机器学习开发环境安装 ·········································································· 10
2.1.1 安装 Anaconda ············································································· 10
2.1.2 安装 JupyterLab ··········································································· 15
2.1.3 安装机器学习库 ··········································································· 18
2.2 基于JupyterLab 环境编程 ······································································· 22
2.2.1 进入 JupyterLab 环境····································································· 22
2.2.2 编写程序 ···················································································· 23
2.2.3 保存及运行程序 ··········································································· 25
2.3 JupyterLab 程序调试 ·············································································· 26
2.3.1 打印变量值 ················································································· 26
2.3.2 JupyterLab Debugger 调试 ······························································· 27
2.4 Markdown 目录制作 ·············································································· 30
小结 ········································································································· 32
习题 ········································································································· 32
模块3 基于K-最近邻的分类预测 ····················
