Word2Vec and FastText Word Embedding with Gensim in Python

Demonstrated how CBOW, Skip-Gram, and FastText capture semantic word relationships effectively.

Compare the performance of CBOW, Skip-Gram, and FastText through word similarity and analogy tasks.

Visualized high-dimensional word embeddings using PCA and t-SNE for easier interpretation.

Established a comprehensive preprocessing pipeline including tokenization, stopword removal, and lemmatization.

Assessed model quality through word similarity, analogy reasoning, and outlier detection tasks.

Applied scalable techniques for processing and training on large text datasets.

Identified domain-specific word patterns and semantic groupings using word embeddings.

Showed how word embeddings can be applied to various NLP tasks like classification and clustering.

Successfully detected outliers in word groups, demonstrating the model's contextual understanding.

Used t-SNE and PCA to compare how different models (CBOW, Skip-Gram, FastText) represent word meanings.