In machine learning model development, feature engineering plays a crucial role since real-world data often comes with noise, missing values, skewed distributions,...

Machine learning model development often feels like navigating a maze, exciting but filled with twists, dead ends, and time sinks.

This post is divided into five parts; they are: • Naive Tokenization • Stemming and Lemmatization • Byte-Pair Encoding (BPE) • WordPiece...

Quantization is a frequently used strategy applied to production machine learning models, particularly large and complex ones, to make them lightweight by...

Machine learning models have become increasingly sophisticated, but this complexity often comes at the cost of interpretability.

This post is divided into three parts; they are: • Understanding Word Embeddings • Using Pretrained Word Embeddings • Training Word2Vec with...