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Feature Engineering for Machine Learning - Alice Zheng

Feature Engineering for Machine Learning - Principles and Techniques for Data Scientists Alice Zheng and Amanda Casari

Feature Engineering for Machine Learning and Data Analytics 无水印原版pdf

Feature Engineering for Machine Learning and Data Analytics 英文无水印原版pdf pdf所有页面使用FoxitReader、PDF-XChangeViewer、SumatraPDF和Firefox测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 查看此书详细信息请在美国亚马逊官网搜索此书

Feature_Engineering_for_Machine_Learning

Feature Engineering for Machine Learning, for kindle. epub

Feature Engineering for Machine Learning_Principl

数据挖掘里面提特征的一些原则和方法，很有用，英文英文

Feature Engineering for Machine Learning_Principles and Techniques

Feature Engineering for Machine Learning_Principles and Techniques for Data Scientists(2018.03).A4

Feature_Engineering_for_Machine_Learning.epub

高清带目录Feature_Engineering_for_Machine_Learning.epub，完整版本，特征工程， Alice Zheng

Feature Engineering

特征工程，特征选择，Feature Engineering for machine learning

Mastering Feature Engineering

Mastering Feature Engineering

feature-engineering-made-easy.pdf

简单的特征工程操作， 轻松处理数据。

Concise Computer Vision An Introduction into Theory and Algorithms

计算机视觉类教材，英文版，带书签，是计算机视觉入门的一本很好的教材

Cloud.Computing.Security.Foundations.and.Challenges

This handbook offers a comprehensive overview of cloud computing security technology and implementation, while exploring practical solutions to a wide range of cloud computing security issues. With more organizations using cloud computing and cloud providers for data operations, proper security in these and other potentially vulnerable areas have become a priority for organizations of all sizes across the globe. Research efforts from both academia and industry in all security aspects related to cloud computing are gathered within one reference guide. Table of Contents SECTION I: Introduction CHAPTER 2: Overview of Cloud Computing CHAPTER 3: Cloud Security Baselines CHAPTER 4: Cloud Security, Privacy, and Trust Baselines CHAPTER 5: Infrastructure as a Service (IaaS) SECTION II: Risk Analysis and Division of Responsibility CHAPTER 7: Managing Risk in the Cloud CHAPTER 8: Cloud Security Risk Management CHAPTER 9: Secure Cloud Risk Management: Risk Mitigation Methods SECTION III: Securing the Cloud Infrastructure CHAPTER 11: Cryptographic Key Management for Data Protection CHAPTER 12: Cloud Security Access Control: Distributed Access Control CHAPTER 13: Cloud Security Key Management: Cloud User Controls CHAPTER 14: Cloud Computing Security Essentials and Architecture CHAPTER 15: Cloud Computing Architecture and Security Concepts CHAPTER 16: Secure Cloud Architecture SECTION IV: Operating System and Network Security CHAPTER 18: Third-Party Providers Integrity Assurance for Data Outsourcing SECTION V: Meeting Compliance Requirements CHAPTER 20: Managing Legal Compliance Risk in the Cloud and Negotiating Personal Data Protection Requirements with Vendors CHAPTER 21: Integrity Assurance for Data Outsourcing CHAPTER 22: Secure Computation Outsourcing CHAPTER 23: Computation Over Encrypted Data CHAPTER 24: Trusted Computing Technology CHAPTER 25: Computing Technology for Trusted Cloud Security CHAPTER 26: Trusted Computing Technology and Proposals for Resolving Cloud Computing Security Problems CHAPTER 27: Assuring Compliance with Government Certification and Accreditation Regulations CHAPTER 28: Government Certification, Accreditation, Regulations, and Compliance Risks SECTION VI: Preparing for Disaster Recovery CHAPTER 30: Availability, Recovery, and Auditing across Data Centers SECTION VII: Advanced Cloud Computing Security CHAPTER 32: Side-Channel Attacks and Defenses on Cloud Traffic CHAPTER 33: Clouds Are Evil CHAPTER 34: Future Directions in Cloud Computing Security: Risks and Challenges

FEATURE ENGINEERING

一份不错的关于机器学习中特征工程的slides。 What is feature engineering? • Limits on number of features • How to select a “good” set of features • Standard FE techniques • TL;DR: As we get better and better models, focus shifts to what we put into them • FE interacts with other key areas of DS

Mastering Feature Engineering(2017.06)

Mastering Feature Engineering(2017.06) Mastering Feature Engineering(2017.06) Mastering Feature Engineering(2017.06)

feature-engineering-for-machine-learning:在线课程“机器学习功能工程”的代码存储库

机器学习的特征工程-代码存储库 在线课程代码存储库 发布于2017年11月，最后更新于2020年12月 目录 简介：变量类型 数值变量：离散和连续 分类变量：标称和序数 日期时间变量 混合变量：字符串和数字 可变特征 缺失数据 基数 类别频率 发行版 离群值 震级 缺少数据插补 均值和中位数插补 任意值估算 尾插补 频繁归类 添加字符串丢失 随机样本插补 添加缺少的指标 用Scikit学习进行插补 使用特征引擎进行归因 多元归因 老鼠 分类变量编码 一种热门编码：简单分类和频繁分类 序数编码：任意和有序 目标均值编码 证据权重 机率 稀有标签编码 使用Scikit学习进行编码 使用功能引擎编码 使用类别编码器编码 变量变换 日志，功率和倒数 Box-Cox 约翰逊 使用Scikit学习进行转型 使用特征引擎进行转换 离散化 随意的 等频离散化 等宽离散 K-均值离散化 树木离散化 使用S

Fundamentals.of.Machine.Learning.for.Predictive.Data.Analytics.02620294

Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals. Table of Contents Chapter 1 Machine Learning for Predictive Data Analytics Chapter 2 Data to Insights to Decisions Chapter 3 Data Exploration Chapter 4 Information-based Learning Chapter 5 Similarity-based Learning Chapter 6 Probability-based Learning Chapter 7 Error-based Learning Chapter 8 Evaluation Chapter 9 Case Study: Customer Churn Chapter 10 Case Study: Galaxy Classification Chapter 11 The Art of Machine Learning for Predictive Data Analytics Appendix A Descriptive Statistics and Data Visualization for Machine Learning Appendix B Introduction to Probability for Machine Learning Appendix C Differentiation Techniques for Machine Learning

Feature Engineering Made Easy (2018.04) (epub+code)

机器学习的利器！ 特征选择的法宝！ kaggle 必备书！ -----Shi Long

ChatGPT教程（终极版）最全整理

这是一篇动了某些人利益的良心教程。 这是一篇姗姗来迟的ChatGPT教程。 纯小白关于ChatGPT入门，你看我这篇文章就够了。 如果你已经用上了ChatGPT，更要恭喜你挖到宝藏，后面的高级技巧一定能让你有收获。 文章包含以下内容： 一、ChatGPT是啥？有什么用； 二、ChatGPT如何注册； 三、ChatGPT使用方法； 四、用ChatGPT搞钱； 五、高级技巧；

博客中Kmeans以及FCM算法数据（免积分）

博客中Kmeans以及FCM算法的数据，包括IRIS鸢尾花数据集、Wine葡萄酒数据集、Seed小麦种子数据集、glass数据集、WDBD乳腺癌数据集，下载在直接存入项目文件夹即可，如果下载不了，可以私信我，看到后会及时回复。

hugging face的models-openai-clip-vit-large-patch14文件夹

用于无法访问hugging face并需要运行stable-diffusion-webui时使用

神经网络回归预测--气温数据集

神经网络回归预测--气温数据集

XGBoost+LightGBM+LSTM-光伏发电量预测

包含比赛代码、数据、训练后的神经网络模型等。 在分析光伏发电原理的基础上，论证了辐照度、光伏板工作温度等影响光伏输出功率的因素，通过实时监测的光伏板运行状态参数和气象参数建立预测模型，预估光伏电站瞬时发电量，根据光伏电站DCS系统提供的实际发电量数据进行对比分析，验证模型的实际应用价值。 1 数据探索与数据预处理 1.1 赛题回顾 1.2 数据探索性分析与异常值处理 1.3 相关性分析 2 特征工程 2.1 光伏发电领域特征 2.2 高阶环境特征 3 模型构建与调试 3.1 预测模型整体结构 3.2 基于LightGBM与XGBoost的构建与调试 3.3 基于LSTM的模型构建与调试 3.4 模型融合与总结 4 总结与展望 参考文献

Mathwork+Matlab+编程手册

Introduction to Programming with MATLAB ~ Vanderbilt University

Stable-Diffusion WEBUI 简体中文语言包（2023.05.30更新）

AI绘图，Stable-Diffusion WEBUI，本地化（简体中文）语言文件。 原始文件来自翻译插件，根据自己实际使用情况，增加和修改了一些翻译。 配合【双语插件】看上去要自然一点，内容还在继续完善中。 本次增加了一些翻译内容，特别是插件。 同时继续合并了其它翻译插件的内容。 最近文字提示修改得有点多啊。 请放入“你的SDWebUI项目位置/localizations/”中。 中文翻译部分删掉了不少括起来的英文原文，所以别直接选它用。 请配合【Bilingual Localization】插件使用，双语同时显示，效果最好。

基于Python+pytorch的图像处理+附完整代码图像处理，能够轻松实现图像的读取、显示、裁剪等还有机器学习等操作

Python+PyTorch：图像处理界的“瑞士军刀” 在图像处理这个充满魔法的世界里，Python和PyTorch这对黄金搭档，就像一位技艺高超的魔法师和一把无所不能的“瑞士军刀”，总能轻松解决各种看似棘手的难题。它们以高效、灵活和强大的特性，引领着图像处理技术的发展潮流，让无数开发者为之倾倒。Python，这位优雅的魔法师，以其简洁易懂的语法和丰富的库资源，赢得了广大开发者喜爱。无论是数据处理、机器学习还是深度学习，Python都能轻松应对，展现出其无与伦比的魅力。在图像处理领域，Python更是如鱼得水，通过OpenCV、PIL等库，能够轻松实现图像的读取、显示、裁剪、缩放、滤波等操作，让图像在指尖起舞。而PyTorch，这把图像处理界的“瑞士军刀”，则以其灵活性和易用性，成为深度学习领域的翘楚。它拥有强大的自动求导功能，能够轻松构建和训练复杂的神经网络模型。在图像处理中，PyTorch能够助力开发者构建出各种高效的图像识别、分割、生成等模型，让图像焕发出新的生机。想象一下，当你掌握了Python和PyTorch这对黄金搭档，就如同掌握了一把魔法杖和一把瑞士军刀。必然大可作为

时间序列预测模型实战案例(Xgboost)(Python)(机器学习)包括时间序列预测和时间序列分类，点击即可运行！

内容概要 资源包括三部分(时间序列预测部分和时间序列分类部分和所需的测试数据集全部包含在内) 在本次实战案例中，我们将使用Xgboost算法进行时间序列预测。Xgboost是一种强大的梯度提升树算法，适用于各种机器学习任务，它最初主要用于解决分类问题，在此基础上也可以应用于时间序列预测。 时间序列预测是通过分析过去的数据模式来预测未来的数值趋势。它在许多领域中都有广泛的应用，包括金融、天气预报、股票市场等。我们将使用Python编程语言来实现这个案例。 其中包括模型训练部分和保存部分，可以将模型保存到本地，一旦我们完成了模型的训练，我们可以使用它来进行预测。我们将选择合适的输入特征，并根据模型的预测结果来生成未来的数值序列。最后，我们会将预测结果与实际观测值进行对比，评估模型的准确性和性能。 适合人群：时间序列预测的学习者,机器学习的学习者， 能学到什么：本模型能够让你对机器学习和时间序列预测有一个清楚的了解，其中还包括数据分析部分和特征工程的代码操作 阅读建议：大家可以仔细阅读代码部分，其中包括每一步的注释帮助读者进行理解，其中涉及到的知识有数据分析部分和特征工程的代码操作。