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Text Mining for AI - Period 4
OVERVIEW
CEA CAPA Partner Institution: Vrije Universiteit Amsterdam
Location: Amsterdam, Netherlands
Primary Subject Area: Computer Sciences
Instruction in: English
Course Code: XB_0085
Transcript Source: Partner Institution
Course Details: Level 300
Recommended Semester Credits: 3
Contact Hours: 84
Prerequisites: Programming in python
DESCRIPTION
Basic concepts from Linguistics and foundational concepts from Natural Language Processing. Skills to use, apply and critically assess text mining techniques. Adapt and build text mining techniques to specific target domains and applications.
Vrije Universiteit Amsterdam (VU Amsterdam) awards credits based on the ECTS system. Contact hours listed under a course description may vary due to the combination of lecture-based and independent work required for each course therefore, CEA's recommended credits are based on the ECTS credits assigned by VU Amsterdam. 1 ECTS equals 28 contact hours assigned by VU Amsterdam.
Vrije Universiteit Amsterdam (VU Amsterdam) awards credits based on the ECTS system. Contact hours listed under a course description may vary due to the combination of lecture-based and independent work required for each course therefore, CEA's recommended credits are based on the ECTS credits assigned by VU Amsterdam. 1 ECTS equals 28 contact hours assigned by VU Amsterdam.
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