Applied ML: Build NLP text embeddings using python

Learn why text embeddings convert unstructured language into numerical representations for NLP and ML, and how one hot, label, and ordinal encoding capture rich word contexts.

Explore tf-idf, word2vec, and GloVe embeddings, including tf-idf weighting, bag-of-words and skip-gram models, one-hot encoding, softmax outputs, and co-occurrence matrix learning.

Compare ELMo and BERT as language model embeddings, highlighting bidirectional context with ELMo's bi-directional LSTM and BERT's transformer-based encoder with masked language modeling for robust embeddings.

Discover why Python powers NLP development with versatile libraries and strong community support. Set up a flexible environment from Google Colab to local setups using virtual environments, Conda, or Docker.

Generate dense text embeddings with tfidf by preprocessing, fitting a tfidf vectorizer, and comparing sentence vectors using cosine similarity.

Explore building word embeddings with FastText in Python using an unsupervised skip-gram model. Evaluate word relationships with cosine similarity, handle out-of-vocabulary words, and save the trained model.

Explore additional embedding libraries beyond the exercises, including Jensen with word and character n-grams and Dr. Beck for sentence embeddings, plus the embeddings library and Hugging Face transformers.