Text Mining
- Offered byThe knowledge academy
Text Mining at The knowledge academy Overview
Duration | 2 days |
Start from | Start Now |
Total fee | ₹29,995 |
Mode of learning | Online |
Official Website | Go to Website  |
Credential | Certificate |
- Overview
- Highlights
- Course Details
- Curriculum
- Admission Process
Text Mining at The knowledge academy Highlights
- Earn a certificate after completion of course
- Engage in activities, and communicate with your trainer and peers
Text Mining at The knowledge academy Course details
Data Scientists
Software Engineers
Data Analysts
Digital Marketers
Product Managers
Business Intelligence Analysts
To grasp the fundamentals of text mining and preprocessing techniques
To utilize key algorithms for efficient text categorization
To evaluate and enhance text classifiers using unlabeled data
To gain expertise in clustering and Information Extraction (IE)
To explore techniques for constraint handling and specification filtering
To master the application of hidden Markov models and maximal entropy Markov models for Information Extraction
With this course master techniques like sentiment analysis, keyword extraction, and topic modelling
Text mining is a knowledge-intensive process that is a pivotal skill in today's information-driven world
It involves interacting with text-based document collections using powerful analysis tools to uncover valuable insights and patterns within vast data sources, be it reports, articles, or social media data
Gain hands-on experience with real-world text data and industry-relevant software
Enhance your data analysis career with expert-led, practical Text Mining Training
Text Mining at The knowledge academy Curriculum
Module 1: Introduction to Text Mining
What is Text Mining
Text Mining Systems Architecture
Module 2: Core Text Mining Operations
What is Core Text Mining Operations
Text Mining Query Languages
Module 3: Text Mining Pre-Processing Techniques
Task-Oriented Approaches
Module 4: Categorization
Text Categorization Applications
Document Representation
Knowledge Engineering to TC
Using Unlabeled Data
Evaluating Text Classifiers
Module 5: Introduction to Clustering
Partitioning of Networks
Clustering Tasks in Text Analysis
Clustering Algorithms
Clustering of Textual Data
Module 6: Information Extraction (IE)
Define Information Extraction
IE Systems Architecture
Anaphora Resolution
IE Inductive Algorithms
Structural Information Extraction (IE)
Module 7: Probabilistic Models for IE
Hidden Markov Models
Stochastic Context-Free Grammar
Maximal Entropy Modelling
Conditional Random Fields
Module 8: Pre-Processing Applications
HMM to Textual Analysis Applications
Using MEMM for IE
Applications of CRFs to Textual Analysis MEMM for IE
Using SCFG Rules
Bootstrapping
Module 9: Presentation-Layer Considerations
Browsing
Accessing Constraints and Simple Specification Filters at the Presentation Layer
Accessing the Underlying Query Language
Module 10: Visualization Approaches
Architectural Considerations
Text Mining Visualization Approaches
Visualization Techniques in Link Analysis
Module 11: Introduction to Link Analysis
Automatic Layout of Networks
Paths and Cycles in Graphs
Centrality
Partitioning of Networks
Networks Pattern Matching
Text Mining at The knowledge academy Admission Process
Important Dates
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