舉報 
會員
Applied Unsupervised Learning with Python
Unsupervisedlearningisausefulandpracticalsolutioninsituationswherelabeleddataisnotavailable.AppliedUnsupervisedLearningwithPythonguidesyouonthebestpracticesforusingunsupervisedlearningtechniquesintandemwithPythonlibrariesandextractingmeaningfulinformationfromunstructureddata.Thecoursebeginsbyexplaininghowbasicclusteringworkstofindsimilardatapointsinaset.Onceyouarewellversedwiththek-meansalgorithmandhowitoperates,you’lllearnwhatdimensionalityreductionisandwheretoapplyit.Asyouprogress,you’lllearnvariousneuralnetworktechniquesandhowtheycanimproveyourmodel.Whilestudyingtheapplicationsofunsupervisedlearning,youwillalsounderstandhowtominetopicsthataretrendingonTwitterandFacebookandbuildanewsrecommendationengineforusers.YouwillcompletethecoursebychallengingyourselfthroughvariousinterestingactivitiessuchasperformingaMarketBasketAnalysisandidentifyingrelationshipsbetweendifferentmerchandises.Bytheendofthiscourse,youwillhavetheskillsyouneedtoconfidentlybuildyourownmodelsusingPython.
目錄(70章)
倒序
- 封面
- 版權頁
- Preface
- About
- About the Book
- Chapter 1 Introduction to Clustering
- Introduction
- Unsupervised Learning versus Supervised Learning
- Clustering
- Introduction to k-means Clustering
- Summary
- Chapter 2 Hierarchical Clustering
- Introduction
- Clustering Refresher
- The Organization of Hierarchy
- Introduction to Hierarchical Clustering
- Linkage
- Agglomerative versus Divisive Clustering
- k-means versus Hierarchical Clustering
- Summary
- Chapter 3 Neighborhood Approaches and DBSCAN
- Introduction
- Introduction to DBSCAN
- DBSCAN Versus k-means and Hierarchical Clustering
- Summary
- Chapter 4 Dimension Reduction and PCA
- Introduction
- Overview of Dimensionality Reduction Techniques
- PCA
- Summary
- Chapter 5 Autoencoders
- Introduction
- Fundamentals of Artificial Neural Networks
- Autoencoders
- Summary
- Chapter 6 t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Introduction
- Stochastic Neighbor Embedding (SNE)
- t-Distributed SNE
- Interpreting t-SNE Plots
- Summary
- Chapter 7 Topic Modeling
- Introduction
- Cleaning Text Data
- Latent Dirichlet Allocation
- Non-Negative Matrix Factorization
- Summary
- Chapter 8 Market Basket Analysis
- Introduction
- Market Basket Analysis
- Characteristics of Transaction Data
- Apriori Algorithm
- Association Rules
- Summary
- Chapter 9 Hotspot Analysis
- Introduction
- Kernel Density Estimation
- Hotspot Analysis
- Summary
- Appendix
- About
- Chapter 1: Introduction to Clustering
- Chapter 2: Hierarchical Clustering
- Chapter 3: Neighborhood Approaches and DBSCAN
- Chapter 4: Dimension Reduction and PCA
- Chapter 5: Autoencoders
- Chapter 6: t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Chapter 7: Topic Modeling
- Chapter 8: Market Basket Analysis
- Chapter 9: Hotspot Analysis 更新時間：2021-06-11 13:24:17
推薦閱讀
- C語言程序設計案例教程
- 兩周自制腳本語言
- Python自動化運維快速入門（第2版）
- 認識編程：以Python語言講透編程的本質
- Internet of Things with the Arduino Yún
- Java Web程序設計
- 高級C/C++編譯技術（典藏版）
- 用Python實現深度學習框架
- Hands-On Reinforcement Learning with Python
- 深入理解Elasticsearch（原書第3版）
- Python極簡講義：一本書入門數據分析與機器學習
- iPhone應用開發從入門到精通
- 深度學習原理與PyTorch實戰（第2版）
- C語言程序設計實訓教程與水平考試指導
- Spring 5 Design Patterns
- 并行編程方法與優化實踐
- Python網絡爬蟲實例教程（視頻講解版）
- Learning Cocos2d-JS Game Development
- Learning NHibernate 4
- AngularJS Web Application Development Cookbook
- 程序員超強大腦
- Android for the BeagleBone Black
- Java編程方法論：響應式RxJava與代碼設計實戰
- HTML5 for Flash Developers
- Getting Started with Meteor.js JavaScript Framework（Second Edition）
- 大學計算機應用基礎教程（第三版）
- 敏捷開發的藝術
- 企業數字化轉型與工業4.0漸進之路：電子元器件行業視角
- Mastering Immutable.js
- 圖解CSS3：核心技術與案例實戰
