Unsupervised Machine Learning Hidden Markov Models in Python

HMMs for stock price analysis, language modeling, web analytics, biology, and PageRank.

4.72 (4361 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Understand and enumerate the various applications of Markov Models and Hidden Markov Models

Understand how Markov Models work

Write a Markov Model in code

Apply Markov Models to any sequence of data

Understand the mathematics behind Markov chains

Apply Markov models to language

Apply Markov models to website analytics

Understand how Google's PageRank works

Understand Hidden Markov Models

Write a Hidden Markov Model in Code

Write a Hidden Markov Model using Theano

Understand how gradient descent, which is normally used in deep learning, can be used for HMMs

Course Gallery

Unsupervised Machine Learning Hidden Markov Models in Python – Screenshot 1 — Screenshot 1 – Unsupervised Machine Learning Hidden Markov Models in Python

Unsupervised Machine Learning Hidden Markov Models in Python – Screenshot 2 — Screenshot 2 – Unsupervised Machine Learning Hidden Markov Models in Python

Unsupervised Machine Learning Hidden Markov Models in Python – Screenshot 3 — Screenshot 3 – Unsupervised Machine Learning Hidden Markov Models in Python

Unsupervised Machine Learning Hidden Markov Models in Python – Screenshot 4 — Screenshot 4 – Unsupervised Machine Learning Hidden Markov Models in Python

Loading charts...

Comidoc Review

Our Verdict

<p>This course offers a comprehensive guide for three kinds of tasks in HMM with real code implementation, particularly excelling in the discrete/continuous HMM sections using deep learning libraries. However, some students may find the theory part dull and unengaging compared to external resources like Rabiner's paper on HMMs. The course could also benefit from more explanation of code design maps and improved interaction when addressing student questions.</p>

What We Liked

Comprehensive guide for three kinds of tasks in HMM with actual code implementation
Deep learning library application sections for discrete/continuous HMM provide new perspective
Well-structured course with clear explanations of complex mathematical concepts
Valuable insights into the mathematics behind Markov chains, language modeling, web analytics, biology, and PageRank

Potential Drawbacks

Some code sections lack explanation of coding design map
Code notebooks were removed from the course and access was not promptly provided upon request
Theory part is dull and unengaging, making it more effective to read external resources such as Rabiner's paper on HMMs
Author's attitude can be incomprehensible and off-putting