This article would focus on techniques for visualizing word embeddings, such as t-SNE and PCA, to gain a better understanding of the semantic relationships captured by these models. It would provide step-by-step instructions on how to create visualizations of embeddings using Python libraries like Matplotlib and Seaborn. The article would explain how to interpret these visualizations, identifying clusters of related words and uncovering hidden semantic patterns. It would also discuss the limitations of visualization techniques and provide guidance on how to avoid misinterpretations. Visualizing embedding spaces helps to develop a stronger understanding of the numerical representation and allows for more detailed debugging of implemented models. Visualizations help both the model developer and the end-user understand how data is clustered, allowing for a more transparent and inclusive decision-making process. It also helps in explaining the relationship between words.