Skip Gram Model Python Implementation for Word Embeddings

Generated meaningful word embeddings capturing semantic relationships between words for improved text analysis.

Enabled dimensionality reduction and visualization to interpret word associations in a simplified 2D space.

Demonstrated the ability to evaluate word similarity using cosine similarity and distance metrics effectively.

Built a scalable model applicable to large datasets through efficient vocabulary management and batching techniques.

Showcased the role of embeddings in powering real-world applications like search engines and recommendation systems.

Enhanced the understanding of neural networks and their role in the semantic representation of natural language data.

Highlighted the importance of preprocessing and its impact on the quality of machine learning outcomes.

Provided a practical approach to embedding visualization, useful for debugging and understanding model behavior.

Validated embeddings for practical NLP tasks like text classification, entity recognition, and sentiment analysis.

Delivered insights into semantic groupings, supporting applications like personalized search and conversational AI.