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BRAND: Expert TRAINING | ENGLISH | INSTANT DOWNLOAD
 
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Data Mining Specialization
 
Content
01_1-1-1-some-books-on-data-visualization
01_1-1-overview-text-mining-and-analytics-part-1
01_1-2-1-2-d-graphics
01_1-3-1-the-human
01_2-1-1-data
01_2-1-syntagmatic-relation-discovery-entropy
01_2-2-1-glyphs-part-1
01_2-3-1-tuftes-design-rules
01_3-1-1-graphs-and-networks
01_3-1-probabilistic-topic-models-mixture-of-unigram-language-models
01_3-2-1-principal-component-analysis
01_3-3-1-packing
01_4-1-1-visualization-systems
01_4-1-text-clustering-motivation
01_4-2-1-visualization-system-design
01_5-1-text-categorization-discriminative-classifier-part-1
01_6-1-opinion-mining-and-sentiment-analysis-latent-aspect-rating-analysis-part-1
01_course-introduction
01_course-introduction
01_course-welcome-video
01_introduction-to-text-mining-and-analytics
01_lesson-1-1-natural-language-content-analysis
01_lesson-2-1-vector-space-model-improved-instantiation
01_lesson-3-1-evaluation-of-tr-systems
01_lesson-4-1-probabilistic-retrieval-model-basic-idea
01_lesson-5-1-feedback-in-text-retrieval
01_lesson-6-1-learning-to-rank-part-1
01_welcome-to-the-data-mining-project
02_1-1-2-overview-of-visualization
02_1-1-what-is-cluster-analysis
02_1-1-what-is-pattern-discovery-why-is-it-important
02_1-2-overview-text-mining-and-analytics-part-2
02_1-3-2-memory
02_2-1-2-mapping
02_2-1-basic-concepts-measuring-similarity-between-objects
02_2-1-the-downward-closure-property-of-frequent-patterns
02_2-2-1-glyphs-part-2
02_2-2-syntagmatic-relation-discovery-conditional-entropy
02_2-3-2-using-color
02_3-1-2-embedding-planar-graphs
02_3-1-limitation-of-the-support-confidence-framework
02_3-1-partitioning-based-clustering-methods
02_3-2-2-multidimensional-scaling
02_3-2-probabilistic-topic-models-mixture-model-estimation-part-1
02_4-1-2-the-information-visualization-mantra-part-1
02_4-1-hierarchical-clustering-methods
02_4-1-mining-multi-level-associations
02_4-2-text-clustering-generative-probabilistic-models-part-1
02_4-6-cure-clustering-using-well-scattered-representatives
02_5-1-density-based-and-grid-based-clustering-methods
02_5-1-sequential-pattern-and-sequential-pattern-mining
02_5-2-text-categorization-discriminative-classifier-part-2
02_6-1-methods-for-clustering-validation
02_6-1-mining-spatial-associations
02_6-2-opinion-mining-and-sentiment-analysis-latent-aspect-rating-analysis-part-2
02_7-1-from-frequent-pattern-mining-to-phrase-mining
02_8-1-frequent-pattern-mining-in-data-streams
02_clustereng-overview
02_course-introduction-video
02_course-prerequisites-completion
02_lesson-1-2-text-access
02_lesson-2-2-tf-transformation
02_lesson-3-2-evaluation-of-tr-systems-basic-measures
02_lesson-4-2-statistical-language-model
02_lesson-5-2-feedback-in-vector-space-model-rocchio
02_lesson-6-2-learning-to-rank-part-2
02_svg-example
02_week-1-introduction
02_week-2-introduction
02_week-3-introduction
02_week-4-introduction
03_1-2-2-2-d-drawing
03_1-2-applications-of-cluster-analysis
03_1-2-frequent-patterns-and-association-rules
03_1-3-3-reasoning
03_1-3-natural-language-content-analysis-part-1
03_2-1-3-charts
03_2-2-2-parallel-coordinates
03_2-2-distance-on-numeric-data-minkowski-distance
03_2-2-the-apriori-algorithm
03_2-3-syntagmatic-relation-discovery-mutual-information-part-1
03_3-1-3-graph-visualization
03_3-2-interestingness-measures-lift-and-kh2
03_3-2-k-means-clustering-method
03_3-3-probabilistic-topic-models-mixture-model-estimation-part-2
03_4-1-2-the-information-visualization-mantra-part-2
03_4-2-agglomerative-clustering-algorithms
03_4-2-mining-multi-dimensional-associations
03_4-3-text-clustering-generative-probabilistic-models-part-2
03_4-7-chameleon-graph-partitioning-on-the-knn-graph-of-the-data
03_5-2-dbscan-a-density-based-clustering-algorithm
03_5-2-gsp-apriori-based-sequential-pattern-mining
03_5-3-text-categorization-evaluation-part-1
03_6-2-clustering-evaluation-measuring-clustering-quality
03_6-2-mining-spatial-colocation-patterns
03_6-3-text-based-prediction
03_7-2-previous-phrase-mining-methods
03_8-2-pattern-discovery-for-software-bug-mining
03_clustereng-k-means-and-k-medoids
03_lesson-1-3-text-retrieval-problem
03_lesson-2-3-doc-length-normalization
03_lesson-3-3-evaluation-of-tr-systems-evaluating-ranked-lists-part-1
03_lesson-4-3-query-likelihood-retrieval-function
03_lesson-5-3-feedback-in-text-retrieval-feedback-in-lm
03_lesson-6-3-learning-to-rank-part-3
04_1-2-3-3-d-graphics
04_1-3-4-the-human-retina
04_1-3-compressed-representation-closed-patterns-and-max-patterns
04_1-3-requirements-and-challenges
04_1-4-natural-language-content-analysis-part-2
04_2-2-3-stacked-graphs-part-1
04_2-3-extensions-or-improvements-of-apriori
04_2-3-proximity-measure-for-symetric-vs-asymmetric-binary-variables
04_2-4-syntagmatic-relation-discovery-mutual-information-part-2
04_3-1-4-tree-maps
04_3-3-initialization-of-k-means-clustering
04_3-3-null-invariance-measures
04_3-4-probabilistic-topic-models-expectation-maximization-algorithm-part-1
04_4-1-2-the-information-visualization-mantra-part-3
04_4-3-divisive-clustering-algorithms
04_4-3-mining-quantitative-associations
04_4-4-text-clustering-generative-probabilistic-models-part-3
04_4-8-probabilistic-hierarchical-clustering
04_5-3-optics-ordering-points-to-identify-clustering-structure
04_5-3-spade-sequential-pattern-mining-in-vertical-data-format
04_5-4-text-categorization-evaluation-part-2
04_6-3-constraint-based-clustering
04_6-3-mining-and-aggregating-patterns-over-multiple-trajectories
04_6-4-contextual-text-mining-motivation
04_7-3-topmine-phrase-mining-without-training-data
04_8-3-pattern-discovery-for-image-analysis
04_clustereng-application-agnes
04_lesson-1-4-overview-of-text-retrieval-methods
04_lesson-2-4-implementation-of-tr-systems
04_lesson-3-4-evaluation-of-tr-systems-evaluating-ranked-lists-part-2
04_lesson-4-4-statistical-language-model-part-1
04_lesson-5-4-web-search-introduction-web-crawler
04_lesson-6-4-future-of-web-search
05_1-2-4-photorealism