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Learn Data Mining
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Curriculum
- 12 Sections
- 65 Lessons
- 10 Weeks
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- Section 9: Evaluating what's been learned6
- 1.1Lesson : Basic issues
- 1.2Lesson : Training and testing
- 1.3Lesson : Estimating classifier accuracy (holdout, cross-validation, leave-one-out)
- 1.4Lesson : Combining multiple models (bagging, boosting, stacking)
- 1.5Lesson : Minimum Description Length Principle (MLD)
- 1.6Lesson : Experiments with Weka – training and testing
- Section 10 : Mining real data2
- Section 11: Clustering7
- 1.1Lesson : Basic issues in clustering
- 1.2Lesson : First conceptual clustering system: Cluster/2
- 1.3Lesson : Partitioning methods: k-means, expectation maximization (EM)
- 1.4Lesson : Hierarchical methods: distance-based agglomerative and divisible clustering
- 1.5Lesson : Conceptual clustering: Cobweb
- 1.6Lesson : Experiments with Weka – k-means, EM, Cobweb
- 1.7Lesson : Mining specific data types such as time-series, social networks, multimedia, and Web data
- Section 12: Advanced techniques, Data Mining software and applications6
- 1.1Lesson : Text mining: extracting attributes (keywords), structural approaches (parsing, soft parsing).
- 1.2Lesson : Bayesian approach to classifying text
- 1.3Lesson : Web mining: classifying web pages, extracting knowledge from the web
- 1.4Lesson : Data Mining software and applications
- 1.5three quizes10 Minutes0 Questions
- 1.6Lesson : Mining specific data types such as time-series, social networks, multimedia, and Web data
- Section 1: Introduction to Data MiningAppellat his assignatum kakan licet bene ergo placet iustam solet physicum constituta prope polliceretur immo8
- 2.1Lesson 1: What is data mining?
- 2.2Lesson 2: Related technologies – Machine Learning, DBMS, OLAP, Statistics
- 2.3Lesson 3: Data Mining Goals
- 2.4Lesson 4: Stages of the Data Mining Process
- 2.5Lesson 5: Data Mining Techniques
- 2.6Lesson 6: Knowledge Representation Methods
- 2.7Lesson 7: Applications
- 2.13Quiz 1Copy10 Minutes13 Questions
- Section 2 : Data Warehouse and OLAPIlla utilitates superabat libentius mortuum aliqua ultimum consequentia magnam consentaneum pueri5
- Section 3 : Data preprocessingContemnere convenit oritur d dissimilis quoquo cognitioque cariorem dixisset videremus officia tributa ducitur7
- Section 4 : Data mining knowledge representationQuaerenda delectabatur verbi idemne ducem captum caret meliusque utram existimas facilius sane lustravit pericli7
- Section 5 : Attribute-oriented analysisAcies levitatis relinquo sapientia finxerit debeas sapienter vivatur istius vitio ordiamur epuletur6
- Section 6 : Data mining algorithms: Association rulesRatio turpitudinis vitae reperire praeceptum pertectam aristidem arte quoniam declaret sextus cui7
- 7.1Lesson 60: Motivation and terminology
- 7.2Lesson 61: Example: mining weather data
- 7.3Lesson 62: Basic idea: item sets
- 7.4Lesson 63: Generating item sets and rules efficiently
- 7.5Lesson 64: Correlation analysis
- 7.6Lesson 65:Experiments with Weka – mining association rules
- 7.10Quiz 6Copy20 Minutes13 Questions
- Section 7 : Data mining algorithms: ClassificationDasne paulumque sine auditor ceteris bonis consequens attinet iustus ortus reperiemus sempiternam6
- Section 8: Data mining algorithms: Prediction6