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The Unsupervised Learning Workshop
最新章節：
9. Hotspot Analysis
DoyoufinditdifficulttounderstandhowpopularcompanieslikeWhatsAppandAmazonfindvaluableinsightsfromlargeamountsofunorganizeddata?TheUnsupervisedLearningWorkshopwillgiveyoutheconfidencetodealwithclutteredandunlabeleddatasets,usingunsupervisedalgorithmsinaneasyandinteractivemanner.Thebookstartsbyintroducingthemostpopularclusteringalgorithmsofunsupervisedlearning.You'llfindouthowhierarchicalclusteringdiffersfromk-means,alongwithunderstandinghowtoapplyDBSCANtohighlycomplexandnoisydata.Movingahead,you'lluseautoencodersforefficientdataencoding.Asyouprogress,you’lluset-SNEmodelstoextracthigh-dimensionalinformationintoalowerdimensionforbettervisualization,inadditiontoworkingwithtopicmodelingforimplementingnaturallanguageprocessing(NLP).Inlaterchapters,you’llfindkeyrelationshipsbetweencustomersandbusinessesusingMarketBasketAnalysis,beforegoingontouseHotspotAnalysisforestimatingthepopulationdensityofanarea.Bytheendofthisbook,you’llbeequippedwiththeskillsyouneedtoapplyunsupervisedalgorithmsoncluttereddatasetstofindusefulpatternsandinsights.
目錄(72章)
倒序
- 封面
- 版權信息
- Preface
- 1. Introduction to Clustering
- Introduction
- Unsupervised Learning versus Supervised Learning
- Clustering
- Introduction to k-means Clustering
- Summary
- 2. Hierarchical Clustering
- Introduction
- Clustering Refresher
- The Organization of the Hierarchy
- Introduction to Hierarchical Clustering
- Linkage
- Agglomerative versus Divisive Clustering
- k-means versus Hierarchical Clustering
- Summary
- 3. Neighborhood Approaches and DBSCAN
- Introduction
- Clusters as Neighborhoods
- Introduction to DBSCAN
- DBSCAN versus k-means and Hierarchical Clustering
- Summary
- 4. Dimensionality Reduction Techniques and PCA
- Introduction
- What Is Dimensionality Reduction?
- Overview of Dimensionality Reduction Techniques
- Principal Component Analysis
- Summary
- 5. Autoencoders
- Introduction
- Fundamentals of Artificial Neural Networks
- Autoencoders
- Summary
- 6. t-Distributed Stochastic Neighbor Embedding
- Introduction
- The MNIST Dataset
- Stochastic Neighbor Embedding (SNE)
- t-Distributed SNE
- Interpreting t-SNE Plots
- Summary
- 7. Topic Modeling
- Introduction
- Topic Models
- Cleaning Text Data
- Latent Dirichlet Allocation
- Non-Negative Matrix Factorization
- Summary
- 8. Market Basket Analysis
- Introduction
- Market Basket Analysis
- Characteristics of Transaction Data
- The Apriori Algorithm
- Association Rules
- Summary
- 9. Hotspot Analysis
- Introduction
- Spatial Statistics
- Kernel Density Estimation
- Hotspot Analysis
- Summary
- Appendix
- 1. Introduction to Clustering
- 2. Hierarchical Clustering
- 3. Neighborhood Approaches and DBSCAN
- 4. Dimensionality Reduction Techniques and PCA
- 5. Autoencoders
- 6. t-Distributed Stochastic Neighbor Embedding
- 7. Topic Modeling
- 8. Market Basket Analysis
- 9. Hotspot Analysis 更新時間：2021-06-18 18:13:09
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