This article explains the fundamentals and mathematics of Hidden Markov Models, illustrates their core components and basic problems, and walks through a complete Python implementation for credit‑card fraud detection, including data preparation, model training, and evaluation.
This article explains Hidden Markov Models, covering their core concepts, basic elements, the three fundamental problems with forward, Viterbi, and Baum‑Welch algorithms, provides a weather illustration, detailed Python code using hmmlearn, and a real‑world earthquake case study, highlighting practical implementation steps.
This article explains how Hidden Markov Models can be used to model Chinese word segmentation, covering the underlying Markov process, model parameters, basic HMM problems, and both supervised and unsupervised training methods.
This article details how to create a simple Chinese pinyin input method using a Hidden Markov Model, training data from Jieba, storing probability matrices in SQLite, and implementing the Viterbi algorithm in Python, complete with code snippets and performance observations.
