book

Applied Unsupervised Learning with Python

Name: Applied Unsupervised Learning with Python
ISBN: 9781789952292

by Benjamin Johnston, Aaron Jones, Christopher Kruger

May 2019

Intermediate to advanced

482 pages

8h 52m

English

Packt Publishing

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Preface
About the BookAbout the AuthorsLearning ObjectivesAudienceApproachHardware RequirementsSoftware RequirementsConventionsInstallation and SetupInstall Anaconda on WindowsInstall Anaconda on LinuxInstall Anaconda on macOSInstall Python on WindowsInstall Python on LinuxInstall Python on macOS XAdditional Resources
Chapter 1
Introduction to ClusteringIntroductionUnsupervised Learning versus Supervised LearningClusteringIdentifying ClustersTwo-Dimensional DataExercise 1: Identifying Clusters in DataIntroduction to k-means ClusteringNo-Math k-means Walkthroughk-means Clustering In-Depth Walkthrough Alternative Distance Metric – Manhattan DistanceDeeper DimensionsExercise 2: Calculating Euclidean Distance in PythonExercise 3: Forming Clusters with the Notion of DistanceExercise 4: Implementing k-means from ScratchExercise 5: Implementing k-means with OptimizationClustering Performance: Silhouette ScoreExercise 6: Calculating the Silhouette ScoreActivity 1: Implementing k-means ClusteringSummary
Chapter 2
Hierarchical ClusteringIntroductionClustering Refresherk-means RefresherThe Organization of HierarchyIntroduction to Hierarchical ClusteringSteps to Perform Hierarchical ClusteringAn Example Walk-Through of Hierarchical ClusteringExercise 7: Building a HierarchyLinkageActivity 2: Applying Linkage CriteriaAgglomerative versus Divisive ClusteringExercise 8: Implementing Agglomerative Clustering with scikit-learnActivity 3: Comparing k-means with Hierarchical Clusteringk-means versus Hierarchical ClusteringSummary
Chapter 3
Neighborhood Approaches and DBSCANIntroductionClusters as NeighborhoodsIntroduction to DBSCANDBSCAN In-DepthWalkthrough of the DBSCAN AlgorithmExercise 9: Evaluating the Impact of Neighborhood Radius SizeDBSCAN Attributes – Neighborhood RadiusActivity 4: Implement DBSCAN from ScratchDBSCAN Attributes – Minimum PointsExercise 10: Evaluating the Impact of Minimum Points ThresholdActivity 5: Comparing DBSCAN with k-means and Hierarchical ClusteringDBSCAN Versus k-means and Hierarchical ClusteringSummary
Chapter 4
Dimension Reduction and PCAIntroductionWhat Is Dimensionality Reduction?Applications of Dimensionality ReductionThe Curse of DimensionalityOverview of Dimensionality Reduction TechniquesDimensionality Reduction and Unsupervised LearningPCAMeanStandard DeviationCovarianceCovariance MatrixExercise 11: Understanding the Foundational Concepts of StatisticsEigenvalues and EigenvectorsExercise 12: Computing Eigenvalues and EigenvectorsThe Process of PCAExercise 13: Manually Executing PCAExercise 14: Scikit-Learn PCAActivity 6: Manual PCA versus scikit-learnRestoring the Compressed DatasetExercise 15: Visualizing Variance Reduction with Manual PCAExercise 16: Visualizing Variance Reduction withExercise 17: Plotting 3D Plots in MatplotlibActivity 7: PCA Using the Expanded Iris DatasetSummary
Chapter 5
AutoencodersIntroductionFundamentals of Artificial Neural NetworksThe NeuronSigmoid FunctionRectified Linear Unit (ReLU)Exercise 18: Modeling the Neurons of an Artificial Neural NetworkActivity 8: Modeling Neurons with a ReLU Activation FunctionNeural Networks: Architecture DefinitionExercise 19: Defining a Keras ModelNeural Networks: TrainingExercise 20: Training a Keras Neural Network ModelActivity 9: MNIST Neural NetworkAutoencodersExercise 21: Simple AutoencoderActivity 10: Simple MNIST AutoencoderExercise 22: Multi-Layer AutoencoderConvolutional Neural NetworksExercise 23: Convolutional AutoencoderActivity 11: MNIST Convolutional AutoencoderSummary
Chapter 6
t-Distributed Stochastic Neighbor Embedding (t-SNE)IntroductionStochastic Neighbor Embedding (SNE)t-Distributed SNEExercise 24: t-SNE MNISTActivity 12: Wine t-SNEInterpreting t-SNE PlotsPerplexityExercise 25: t-SNE MNIST and PerplexityActivity 13: t-SNE Wine and PerplexityIterationsExercise 26: t-SNE MNIST and IterationsActivity 14: t-SNE Wine and IterationsFinal Thoughts on VisualizationsSummary
Chapter 7
Topic ModelingIntroductionTopic ModelsExercise 27: Setting Up the EnvironmentA High-Level Overview of Topic ModelsBusiness ApplicationsExercise 28: Data LoadingCleaning Text DataData Cleaning TechniquesExercise 29: Cleaning Data Step by StepExercise 30: Complete Data CleaningActivity 15: Loading and Cleaning Twitter DataLatent Dirichlet AllocationVariational InferenceBag of WordsExercise 31: Creating a Bag-of-Words Model Using the Count VectorizerPerplexityExercise 32: Selecting the Number of TopicsExercise 33: Running Latent Dirichlet AllocationExercise 34: Visualize LDAExercise 35: Trying Four TopicsActivity 16: Latent Dirichlet Allocation and Health TweetsBag-of-Words Follow-UpExercise 36: Creating a Bag-of-Words Using TF-IDFNon-Negative Matrix FactorizationFrobenius NormMultiplicative UpdateExercise 37: Non-negative Matrix FactorizationExercise 38: Visualizing NMFActivity 17: Non-Negative Matrix FactorizationSummary
Chapter 8
Market Basket AnalysisIntroductionMarket Basket AnalysisUse CasesImportant Probabilistic MetricsExercise 39: Creating Sample Transaction DataSupportConfidenceLift and LeverageConvictionExercise 40: Computing MetricsCharacteristics of Transaction DataExercise 41: Loading DataData Cleaning and FormattingExercise 42: Data Cleaning and FormattingData EncodingExercise 43: Data EncodingActivity 18: Loading and Preparing Full Online Retail DataApriori AlgorithmComputational FixesExercise 44: Executing the Apriori algorithmActivity 19: Apriori on the Complete Online Retail DatasetAssociation RulesExercise 45: Deriving Association RulesActivity 20: Finding the Association Rules on the Complete Online Retail DatasetSummary
Chapter 9
Hotspot AnalysisIntroductionSpatial StatisticsProbability Density FunctionsUsing Hotspot Analysis in BusinessKernel Density EstimationThe Bandwidth ValueExercise 46: The Effect of the Bandwidth ValueSelecting the Optimal BandwidthExercise 47: Selecting the Optimal Bandwidth Using Grid SearchKernel FunctionsExercise 48: The Effect of the Kernel FunctionKernel Density Estimation DerivationExercise 49: Simulating the Derivation of Kernel Density EstimationActivity 21: Estimating Density in One DimensionHotspot AnalysisExercise 50: Loading Data and Modeling with SeabornExercise 51: Working with BasemapsActivity 22: Analyzing Crime in LondonSummary

Appendix
Chapter 1: Introduction to ClusteringActivity 1: Implementing k-means ClusteringChapter 2: Hierarchical ClusteringActivity 3: Comparing k-means with Hierarchical ClusteringChapter 3: Neighborhood Approaches and DBSCANActivity 4: Implement DBSCAN from ScratchActivity 5: Comparing DBSCAN with k-means and Hierarchical ClusteringChapter 4: Dimension Reduction and PCAActivity 6: Manual PCA versus scikit-learnActivity 7: PCA Using the Expanded Iris DatasetChapter 5: AutoencodersActivity 8: Modeling Neurons with a ReLU Activation FunctionActivity 9: MNIST Neural NetworkActivity 10: Simple MNIST AutoencoderActivity 11: MNIST Convolutional AutoencoderChapter 6: t-Distributed Stochastic Neighbor Embedding (t-SNE)Activity 12: Wine t-SNEActivity 13: t-SNE Wine and PerplexityActivity 14: t-SNE Wine and IterationsChapter 7: Topic ModelingActivity 15: Loading and Cleaning Twitter DataActivity 16: Latent Dirichlet Allocation and Health TweetsActivity 17: Non-Negative Matrix FactorizationChapter 8: Market Basket AnalysisActivity 18: Loading and Preparing Full Online Retail DataActivity 19: Apriori on the Complete Online Retail DatasetActivity 20: Finding the Association Rules on the Complete Online Retail DatasetChapter 9: Hotspot AnalysisActivity 21: Estimating Density in One DimensionActivity 22: Analyzing Crime in London

Content preview from Applied Unsupervised Learning with Python

Chapter 3 Neighborhood Approaches and DBSCAN

Learning Objectives

By the end of this chapter, you will be able to:

Understand how neighborhood approaches to clustering work from beginning to end
Implement the DBSCAN algorithm from scratch by using packages
Identify the best suited algorithm from k-means, hierarchical clustering, and DBSCAN to solve your problem

In this chapter, we will have a look at DBSCAN clustering approach that will serve us best in the highly complex data.

Introduction

So far, we have covered two popular ways of approaching the clustering problem: k-means and hierarchical clustering. Both clustering techniques have pros and cons associated with how they are carried out. Once again, let's revisit where we have been ...

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Applied Unsupervised Learning with Python

by Benjamin Johnston, Aaron Jones, Christopher Kruger

Chapter 3

Neighborhood Approaches and DBSCAN

Learning Objectives

Introduction

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