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人工智能数据素养 版权信息
- ISBN：9787121444234
- 条形码：9787121444234 ; 978-7-121-44423-4
- 装帧：一般胶版纸
- 册数：暂无
- 重量：暂无
- 所属分类：>
人工智能数据素养 本书特色
本书以人工智能下的大数据时代为背景，从数据素养、数据分析基础、统计分析、机器学习多个维度全面系统地介绍了如何探索数据、整理数据并分析数据。
人工智能数据素养 内容简介
数据作为一种新型生产要素，在未来的社会发展过程中将扮演越来越重要的角色，提升数据素养将有助于促进中国人工智能后备人才的高质量发展。本书以人工智能下的大数据时代为背景，从数据素养、数据分析基础、统计分析、机器学习多个维度全面系统地介绍了如何探索数据、整理数据并分析数据。本书没有给出晦涩难懂的数学公式，也不涉及复杂烦琐的程序代码，而是在阐述基本原理的基础上，辅以简洁的Python 程序，让读者能够快速入门，提升个人的数据综合素养。
人工智能数据素养 目录
第1 章 人工智能下的大数据时代 ...................................................................................... 001
1.1 大数据时代和人工智能 ····································································.001
1.1.1 一切皆为数据 ······································································.001
1.1.2 数据高速增长时代 ································································.002
1.1.3 利用人工智能掘金大数据························································.003
1.2 人工智能三要素 ·············································································.004
1.2.1 数据——AI 之源 ···································································.005
1.2.2 算法——AI 之核 ···································································.006
1.2.3 算力——AI 之驱 ···································································.007
1.3 数据素养 ······················································································.007
1.3.1 何为数据素养 ······································································.007
1.3.2 数据素养为何重要 ································································.010
1.3.3 如何提升数据素养 ································································.011
1.4 本章小结 ······················································································.012
第2 章 Python 数据分析基础 ............................................................................................. 013
2.1 Python 基础 ··················································································.013
2.1.1 Python 简介 ·········································································.013
2.1.2 Python 数据类型 ···································································.017
2.1.3 常用的操作、函数和方法························································.021
2.1.4 列表、元组、字典 ································································.024
2.1.5 顺序结构 ············································································.027
2.1.6 分支结构 ············································································.027
2.1.7 循环结构 ············································································.030
2.2 Python 数据分析环境 ······································································.032
2.2.1 使用pip 安装数据分析相关库 ··················································.032
2.2.2 安装Anaconda ·····································································.033
2.3 Python 数据分析相关库 ···································································.033
2.3.1 NumPy 库 ··········································································.033
2.3.2 Matplotlib 库 ·······································································.034
2.3.3 SciPy 库·············································································.035
2.3.4 Pandas 库 ···········································································.036
2.3.5 xlrd 库 ···············································································.036
2.3.6 PyMySQL 库 ·······································································.037
2.3.7 其他数据分析相关库 ·····························································.037
2.4 本章小结 ·····················································································.038
第3 章 Jupyter 环境的使用 ................................................................................................. 039
3.1 Jupyter Notebook 概述 ·····································································.039
3.1.1 Jupyter Notebook 简介及优点 ···················································.039
3.1.2 Jupyter Notebook 开发环境的搭建 ·············································.039
3.1.3 使用pip 命令安装 ································································.044
3.2 认识Jupyter Notebook ·····································································.044
3.2.1 认识Files、Running、Clusters 页面 ··········································.044
3.2.2 认识Jupyter Notebook 的主页面 ···············································.046
3.3 新建、运行、保存Jupyter Notebook 文件 ·············································.048
3.3.1 新建一个Jupyter Notebook ······················································.0
展开全部
人工智能数据素养 作者简介
孙越，上海外国语大学附属龙岗学校校长，长期从事教育信息化工作和智慧校园建设。目前担任中国发明协会中小学创造教育分会常务理事，深圳教育学会教育信息化和人工智能专委会副理事长，香港中文大学(深圳)特聘导师。同时获评上海市“普教系统名校长名师培养工程——攻关计划”人才、上海市教育科研专家库专家、深圳市龙岗区卓越校长、深圳市龙岗区“名师工作室”主持人等。 龚超，日本工学博士，清华大学日本研究中心主任助理，深圳清华大学研究院下一代互联网研发中心核心成员，未来基因(北京)人工智能研究院首席专家，教育部教育信息化教学应用实践共同体项目特聘专家。中国高科技产业化研究会理事，中国人工智能学会中小学工作委员会委员，中国自动化学会普及工作委员会委员。研究方向为人工智能优化算法，人工智能在数字化转型中的应用等。多家500强企业数字化转型领域高级顾问，在国内外期刊上发表文章共计60余篇。 袁中果，中国人民大学附属中学信息技术教研组组长，中国人工智能学会中小学工作委员会秘书长，中国自动化学会普及工作委员会副主任委员，北京市特级教师，中国人民大学博士，海淀区名师工作站导师，海淀区优秀种子教师工作站实践导师，海淀区督学。
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