舉報(bào) 
會(huì)員
Machine Learning for Data Mining
Machinelearning(ML)combinedwithdataminingcangiveyouamazingresultsinyourdataminingworkbyempoweringyouwithseveralwaystolookatdata.Thisbookwillhelpyouimproveyourdataminingtechniquesbyusingsmartmodelingtechniques.ThisbookwillteachyouhowtoimplementMLalgorithmsandtechniquesinyourdataminingwork.Itwillenableyoutopairthebestalgorithmswiththerighttoolsandprocesses.Youwilllearnhowtoidentifypatternsandmakepredictionswithminimalhumanintervention.YouwillbuilddifferenttypesofMLmodels,suchastheneuralnetwork,theSupportVectorMachines(SVMs),andtheDecisiontree.Youwillseehowallofthesemodelsworksandwhatkindofdatainthedatasettheyaresuitedfor.Youwilllearnhowtocombinetheresultsofdifferentmodelsinordertoimproveaccuracy.Topicssuchasremovingnoiseandhandlingerrorswillgiveyouanaddededgeinmodelbuildingandoptimization.Bytheendofthisbook,youwillbeabletobuildpredictivemodelsandextractinformationofinterestfromthedataset
目錄(89章)
倒序
- coverpage
- Title Page
- Copyright and Credits
- Machine Learning for Data Mining
- Contributors
- About the author
- Packt is searching for authors like you
- About Packt
- Why subscribe?
- Packt.com
- Preface
- Who this book is for
- What this book covers
- To get the most out of this book
- Download the example code files
- Download the color images
- Conventions used
- Get in touch
- Reviews
- Introducing Machine Learning Predictive Models
- Characteristics of machine learning predictive models
- Types of machine learning predictive models
- Working with neural networks
- Advantages of neural networks
- Disadvantages of neural networks
- Representing the errors
- Types of neural network models
- Multi-layer perceptron
- Why are weights important?
- An example representation of a multilayer perceptron model
- The linear regression model
- A sample neural network model
- Feed-forward backpropagation
- Model training ethics
- Summary
- Getting Started with Machine Learning
- Demonstrating a neural network
- Running a neural network model
- Interpreting results
- Analyzing the accuracy of the model
- Model performance on testing partition
- Support Vector Machines
- Working with Support Vector Machines
- Kernel transformation
- But what is the best solution?
- Types of kernel functions
- Demonstrating SVMs
- Interpreting the results
- Trying additional solutions
- Summary
- Understanding Models
- Models
- Statistical models
- Decision tree models
- Machine learning models
- Using graphs to interpret machine learning models
- Using statistics to interpret machine learning models
- Understanding the relationship between a continuous predictor and a categorical outcome variable
- Using decision trees to interpret machine learning models
- Summary
- Improving Individual Models
- Modifying model options
- Using a different model to improve results
- Removing noise to improve models
- How to remove noise
- Doing additional data preparation
- Preparing the data
- Balancing data
- The need for balancing data
- Implementing balance in data
- Summary
- Advanced Ways of Improving Models
- Combining models
- Combining by voting
- Combining by highest confidence
- Implementing combining models
- Combining models in Modeler
- Combining models outside Modeler
- Using propensity scores
- Implementations of propensity scores
- Meta-level modeling
- Error modeling
- Boosting and bagging
- Boosting
- Bagging
- Predicting continuous outcomes
- Summary
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時(shí)間：2021-06-24 14:50:49
推薦閱讀
- Ansible Configuration Management
- 中文版Photoshop CS5數(shù)碼照片處理完全自學(xué)一本通
- Circos Data Visualization How-to
- Learning Microsoft Azure Storage
- Hands-On Data Science with SQL Server 2017
- 自主研拋機(jī)器人技術(shù)
- AWS Certified SysOps Administrator：Associate Guide
- CompTIA Linux+ Certification Guide
- Docker High Performance（Second Edition）
- JSP從入門到精通
- 精通數(shù)據(jù)科學(xué)算法
- 電腦主板現(xiàn)場(chǎng)維修實(shí)錄
- 大數(shù)據(jù)驅(qū)動(dòng)的機(jī)械裝備智能運(yùn)維理論及應(yīng)用
- 嵌入式操作系統(tǒng)原理及應(yīng)用
- 運(yùn)動(dòng)控制系統(tǒng)（第2版）
- Practical AWS Networking
- 樂高創(chuàng)意機(jī)器人教程（中級(jí) 上冊(cè) 10~16歲） (青少年iCAN+創(chuàng)新創(chuàng)意實(shí)踐指導(dǎo)叢書)
- Hands-On Microservices with C#
- PostgreSQL High Performance Cookbook
- 時(shí)序大數(shù)據(jù)平臺(tái)TDengine核心原理與實(shí)戰(zhàn)
- 華人動(dòng)畫師的法蘭西印象
- 人工智能算法（卷2）：受大自然啟發(fā)的算法
- 數(shù)字媒體交互設(shè)計(jì)原理與方法
- ARM Cortex-M3嵌入式開發(fā)實(shí)例詳解
- 玩中學(xué)：樂高機(jī)器人入門（上冊(cè)） (中小學(xué)創(chuàng)客教育執(zhí)委會(huì)推薦教材)
- C語言程序設(shè)計(jì)任務(wù)驅(qū)動(dòng)式教程（第2版）（微課版）
- 樂高創(chuàng)意機(jī)器人教程（初級(jí) 下冊(cè) 6-12歲） (青少年iCAN+創(chuàng)新創(chuàng)意實(shí)踐指導(dǎo)叢書)
- Hands-On Deep Learning Architectures with Python
- 人本智造：工業(yè)5.0的核心使能技術(shù)
- Photoshop CS3中文版圖像處理與平面設(shè)計(jì)精彩百練
