Course

Course Summary
Credit Type:
Course
ACE ID:
STAT-0018
Organization's ID:
509
Organization:
Statistics.com
Length:
4 weeks (60 hours)
Dates Offered:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Upper-Division Baccalaureate 3 introduction to natural language processing
Description

Objective:

The course objective is to teach about deep neural networks, and how to use them in processing text with Python (Natural Language Processing or NLP).

Learning Outcomes:

  • identify the different components of a neural network and explain the flow of data through the network
  • employ pre-trained models to improve model performance and shorten development time
  • use recurrent neural networks for sequential learning (sequence-to-sequence modeling)
  • deploy attention models to improve predictive performance
  • represent words as binary vectors using different models
  • explain, at a high level, the structure of a convolutional neural network
  • specify and code different sequential neural network models for NLP

General Topics:

  • Introduction to deep learning and representation learning
  • Context sensitive learning: convnets and sequential models
  • Deep transfer learning for NLP
  • Attention, transformers and applications
Instruction & Assessment

Instructional Strategies:

  • Computer Based Training
  • Discussion
  • Practical Exercises

Methods of Assessment:

  • Quizzes
  • Project

Minimum Passing Score:

80%
Supplemental Materials