实用机器学习

Feature_Engineering_for_Machine_Learning

Feature Engineering for Machine Learning, for kindle. epub

Feature Engineering for Machine Learning

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

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

《精通特征工程》示例代码.zip

《精通特征工程》 / 《Feature Engineering for Machine Learning》书中的示例代码

Feature Engineering for Machine Learning - Alice Zheng

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

基于快速背景差分的高速铁路异物侵入检测算法_郭保青.pdf

论文仅供学习和参考。 针对铁路场景抖动发生在垂直方向的特点，提出了一维灰度投影结合高斯滤波的图像快速去抖方法，在大幅提高处理速度的同时获得了较好的去抖效果; 针对复杂多变的背景，提出了一种基于前景目标统计分布的背景更新算法，定义了目标分散指数用于确定行列投影次序，通过统计前景目标分布实现背景更新，在提高速度的同时解决了传统背景更新算法难以解决的鬼影问题。最后通过背景差分获取前景目标，并通过目标标记、合并与特性分析提高目标检测的准确性。

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

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

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

feature-engineering-made-easy.pdf

简单的特征工程操作， 轻松处理数据。

Feature Engineering Made Easy(2018.01).A4.pdf

Feature Engineering Made Easy(2018.01).A4.pdf 让特征工程变的简单 预览文字版本，排版有点差，英文原版，简单易读

特征工程选择_整理

特征工程是什么？ 数据和特征决定了机器学习的上限，而模型和算法只是逼近这个上限 一项工程活动，目的是最大限度地从原始数据中提取特征以供算法和模型使用。

极佳的特征工程教程——Understanding Feature Engineering

特征工程是机器学习建模最重要的一步，它直接决定了模型的效果。 近日数据科学家 Dipanjan Sarkar发表了几篇特征工程的深度好文，强烈推荐大家学习观摩！

Mastering Feature Engineering

Mastering Feature Engineering

201905—机器学习之特征工程—六天课程知识总结的思维导图.pdf

201905—机器学习之特征工程—六天课程知识总结的思维导图.pdf

机器学习特征工程基本技术

机器学习特征工程基本技术，Fundamental Techniques of Feature Engineering for Machine Learning。

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

特征工程思维导图

机器学习的前置工程应用 特征工程思维导图

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

Mastering Feature Engineering(2017.06)

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

很经典软件工程精通，软件工程

软件工程精通，很好的软件工程书籍，很经典

Mastering-Feature-Engineering-Principles-Techniques.pdf

The Machine Learning Pipeline 10 Data 11 Tasks 11 Models 12 Features 13 2. Basic Feature Engineering for Text Data: Flatten and Filter. . . . . . . . . . . . . . . . . . . . . . . 15 Turning Natural Text into Flat Vectors 15 Bag-of-words 16 Implementing bag-of-words: parsing and tokenization 20 Bag-of-N-Grams 21 Collocation Extraction for Phrase Detection 23 Quick summary 26 Filtering for Cleaner Features 26 Stopwords 26 Frequency-based filtering 27 Stemming 30 Summary 31 3. The Effects of Feature Scaling: From Bag-of-Words to Tf-Idf. . . . . . . . . . . . . . . . . . . . . . . 33 Tf-Idf : A Simple Twist on Bag-of-Words 33 Feature Scaling 35 Min-max scaling 35 Standardization (variance scaling) 36 L2 normalization 37 iii www.it-ebooks.info Putting it to the Test 38 Creating a classification dataset 39 Implementing tf-idf and feature scaling 40 First try: plain logistic regression 42 Second try: logistic regression with regularization 43 Discussion of results 46 Deep Dive: What is Happening? 47

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

Feature Engineering for Machine Learning_Principles and Techniques

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

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