Natural Language Processing (NLP) is a field of study that focuses on enabling computers to understand and process human language. It involves techniques and algorithms for tasks like text classification, sentiment analysis, named entity recognition, machine translation, question answering, and text summarization. NLP algorithms analyze and interpret textual data, allowing for language understanding and generation. NLP skills include feature extraction, language modeling, neural networks, sequence labeling, and deep learning architectures like transformers.
- Text Preprocessing: Cleaning and preparing textual data for analysis. Understanding the concepts for tokenization, stop word removal, stemming, and lemmatization.
- Part-of-Speech (POS) Tagging: Assigning grammatical tags to words in a sentence
- Named Entity Recognition (NER): Identifying named entities in text, such as people, places, or organizations using pre-trained models.
- Sentiment Analysis: Determining the sentiment of a text, whether it's positive, negative, or neutral
- Text Classification: Categorizing text into predefined classes or categories
- 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. Understanding the basic concepts for activation functions, loss functions, and optimization algorithms.