Natural Language Processing

NLP is a subfield of artificial intelligence and computational linguistics that focuses on the interaction between computers and human language. NLP involves the development of algorithms and techniques to enable computers to understand, interpret, and generate human language in a way that is both meaningful and useful.

This competency area includes an understanding of the concepts of text preprocessing, POS tagging, named entity recognition, sentiment analysis, text classification, and basic neural networks.

Key Competencies:

1. Text Preprocessing - Ability to clean and prepare textual data for analysis using tokenization, stop word removal, stemming, and lemmatization techniques. 

2. Part-of-Speech (POS) Tagging - Understand the syntactic structure of sentences, enabling better analysis and understanding of the text. 

3. Named Entity Recognition (NER) - Ability to extract valuable information from unstructured data using these models that are trained to recognize and label specific entities within the text

4. Sentiment Analysis - Ability to determine the sentiment of a text, whether it's positive, negative, or neutral

5. Text Classification - Ability to categorize the text into predefined classes or categories

6. Basic Neural Networks - Understanding the architecture of neural networks and how to build and train them using deep learning libraries such as TensorFlow, Keras, and PyTorch.