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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
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