Post-Training Quantization in Practice | Edge ML | PAMCET

Master Post-Training Quantization and learn how to compress machine learning models for edge deployment — no prior ML compression experience needed! What You Will Learn: • What post-training quantization is and why it matters for edge devices • How quantization reduces model size without retraining from scratch • The practical steps involved in applying quantization to a trained model • How quantization fits within the broader model compression pipeline • When to use post-training quantization versus other compression techniques Key Topics Covered: Post-Training Quantization, Model Compression, Quantization vs Pruning, Edge AI Optimization, Efficient Model Deployment This lecture is part of the Model Compression: Quantization and Pruning chapter in the Edge Machine Learning course on PAMCET — designed for beginners who want real, practical understanding of deploying AI on edge devices. Enroll at https://pamcet.com PAMCET — Nigeria's No.1 Online Learning Platform

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