Text Mining and Analytics

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Text Mining and Analytics

This course will cover the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort.

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workload

12h

premium access

7 days

created on

04/12/2017

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Syllabus:

Aula #1 - Overview Text Mining and Analytics - Part 1
Aula #2 - Overview Text Mining and Analytics - Part 2
Aula #3 - Natural Language Content Analysis - Part 1
Aula #4 - Natural Language Content Analysis - Part 2
Aula #5 - Text Representation - Part 1
Aula #6 - Text Representation - Part 2
Aula #7 - Word Association Mining and Analysis
Aula #8 - Paradigmatic Relation Discovery - Part 1
Aula #9 - Paradigmatic Relation Discovery - Part 2
Aula #10 - Syntagmatic Relation Discovery Entropy
Aula #11 - Syntagmatic Relation Discovery Conditional Entropy
Aula #12 - Syntagmatic Relation Discovery Mutual Information - Part 1
Aula #13 - Syntagmatic Relation Discovery Mutual Information - Part 2
Aula #14 - Topic Mining and Analysis Motivation and Task Definition
Aula #15 - Topic Mining and Analysis Term as Topic
Aula #16 - Topic Mining and Analysis Probabilistic Topic Models
Aula #17 - Probabilistic Topic Models Overview of Statistical Language Models - Part 1
Aula #18 - Probabilistic Topic Models Overview of Statistical Language Models - Part 2
Aula #19 - Probabilistic Topic Models Mining One Topic
Aula #20 - Probabilistic Topic Models Mixture of Unigram Language Models
Aula #21 - Probabilistic Topic Models Mixture Model Estimation - Part 1
Aula #22 - Probabilistic Topic Models Mixture Model Estimation - Part 2
Aula #23 - Probabilistic Topic Models Expectation Maximization Algorithm - Part 1
Aula #24 - Probabilistic Topic Models Expectation Maximization Algorithm - Part 2
Aula #25 - Probabilistic Topic Models Expectation Maximization Algorithm - Part 3
Aula #26 - Probabilistic Latent Semantic Analysis PLSA - Part 1
Aula #27 - Probabilistic Latent Semantic Analysis PLSA - Part 2
Aula #28 - Latent Dirichlet Allocation LDA - Part 1
Aula #29 - Latent Dirichlet Allocation LDA - Part 2
Aula #30 - Text Clustering Motivation
Aula #31 - Text Clustering Generative Probabilistic Models Part 1
Aula #32 - Text Clustering Generative Probabilistic Models Part 2
Aula #33 - Text Clustering Generative Probabilistic Models Part 3
Aula #34 - Text Clustering Similarity based Approaches
Aula #35 - Text Clustering Evaluation
Aula #36 - Text Categorization Motivation
Aula #37 - Text Categorization Methods
Aula #38 - Text Categorization Generative Probabilistic Models
Aula #39 - Text Categorization Discriminative Classifier Part 1
Aula #40 - Text Categorization Discriminative Classifier Part 2
Aula #41 - Text Categorization Evaluation Part 1
Aula #42 - Text Categorization Evaluation Part 2
Aula #43 - Opinion Mining and Sentiment Analysis Motivation
Aula #44 - Opinion Mining and Sentiment Analysis Sentiment Classification
Aula #45 - Opinion Mining and Sentiment Analysis Ordinal Logistic Regression
Aula #46 - Opinion Mining and Sentiment Analysis Latent Aspect Rating Analysis Part 1
Aula #47 - Opinion Mining and Sentiment Analysis Latent Aspect Rating Analysis Part 2
Aula #48 - Text Based Prediction
Aula #49 - Contextual Text Mining Motivation
Aula #50 - Contextual Text Mining Contextual Probabilistic Latent Semantic Analysis
Aula #51 - Contextual Text Mining Mining Topics with Social Network Context
Aula #52 - Contextual Text Mining Mining Casual Topics with Time Series Supervision
Aula #53 - Course Summary
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Modules & lessons

Module 1: Text Mining and Analytics

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  • 12 CLASS HOURS
  • 30 free access days*
  • Grace period of 120 days**
  • Certificate of completion***
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