Machine learning models have become increasingly complex and resource-intensive, posing significant challenges. Model compression techniques addresses this issue by reducing the size and computational requirements of these models without significant loss in performance. This talk gives a comprehensive overview and code walk through of machine learning model compression techniques. (Model Pruning, Knowledge Distillation, Quantization, and low-rank approximation etc)