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.
- Language Modeling: Building statistical or neural models to predict the likelihood of text sequences
- Topic Modeling: Discovering topics or themes in a collection of text documents
- Sequence Labeling: Assigning labels to each element in a sequence of data, such as named entity recognition or part-of-speech tagging
- Machine Translation: Translating text from one language to another using statistical or neural models
- Named Entity Disambiguation: Resolving ambiguous named entities in text, such as determining which person a pronoun refers to
- Advanced Text Generation: Generating complex and coherent texts, such as stories or essays
- Recurrent Neural Networks (RNNs): Understanding the architecture of RNNs and how to build and train them using deep learning libraries such as TensorFlow, Keras, or PyTorch. Understanding the basic concepts for LSTM and GRU cells, sequence-to-sequence models, and language models.
Word Embeddings - Representing words as vectors in a high-dimensional space using Python libraries such as Word2Vec, GloVe, or FastText. Understanding the API concepts for training and using word embedding models.