Unsupervised Machine Learning Hidden Markov Models in Python
4.6 (2,619 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
20,425 students enrolled

Unsupervised Machine Learning Hidden Markov Models in Python

HMMs for stock price analysis, language modeling, web analytics, biology, and PageRank.
4.6 (2,619 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
20,425 students enrolled
Last updated 7/2020
English [Auto], Portuguese [Auto]
Current price: $139.99 Original price: $199.99 Discount: 30% off
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This course includes
  • 9 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll 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 content
Expand all 63 lectures 09:04:53
+ Markov Models
3 lectures 16:56
Markov Models
The Math of Markov Chains
+ Markov Models: Example Problems and Applications
6 lectures 36:25
Example Problem: Sick or Healthy
Example Problem: Expected number of continuously sick days
Example application: SEO and Bounce Rate Optimization
Example Application: Build a 2nd-order language model and generate phrases
Example Application: Google’s PageRank algorithm
Suggestion Box
+ Hidden Markov Models for Discrete Observations
19 lectures 02:54:24
From Markov Models to Hidden Markov Models
HMM - Basic Examples
Parameters of an HMM
The 3 Problems of an HMM
The Forward-Backward Algorithm (part 1)
The Forward-Backward Algorithm (part 2)
The Forward-Backward Algorithm (part 3)
The Viterbi Algorithm (part 1)
The Viterbi Algorithm (part 2)
HMM Training (part 1)
HMM Training (part 2)
HMM Training (part 3)
HMM Training (part 4)
How to Choose the Number of Hidden States
Baum-Welch Updates for Multiple Observations
Discrete HMM in Code
The underflow problem and how to solve it
Discrete HMM Updates in Code with Scaling
Scaled Viterbi Algorithm in Log Space
+ Discrete HMMs Using Deep Learning Libraries
6 lectures 54:10
Gradient Descent Tutorial
Theano Scan Tutorial
Discrete HMM in Theano
Improving our Gradient Descent-Based HMM
Tensorflow Scan Tutorial
Discrete HMM in Tensorflow
+ HMMs for Continuous Observations
6 lectures 01:00:34
Gaussian Mixture Models with Hidden Markov Models
Generating Data from a Real-Valued HMM
Continuous-Observation HMM in Code (part 1)
Continuous-Observation HMM in Code (part 2)
Continuous HMM in Theano
Continuous HMM in Tensorflow
+ HMMs for Classification
2 lectures 13:06
Generative vs. Discriminative Classifiers
HMM Classification on Poetry Data (Robert Frost vs. Edgar Allan Poe)
+ Bonus Example: Parts-of-Speech Tagging
2 lectures 10:58


Data is from: http://www.cnts.ua.ac.be/conll2000/chunking/

Code is in the same repo as this course, but in the folder nlp_class2

Parts-of-Speech Tagging Concepts
POS Tagging with an HMM
+ Theano, Tensorflow, and Machine Learning Basics Review
3 lectures 18:18
(Review) Gaussian Mixture Models
(Review) Theano Tutorial
(Review) Tensorflow Tutorial
+ Setting Up Your Environment
2 lectures 37:52
Windows-Focused Environment Setup 2018
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
  • Familiarity with probability and statistics
  • Understand Gaussian mixture models
  • Be comfortable with Python and Numpy

The Hidden Markov Model or HMM is all about learning sequences.

A lot of the data that would be very useful for us to model is in sequences. Stock prices are sequences of prices. Language is a sequence of words. Credit scoring involves sequences of borrowing and repaying money, and we can use those sequences to predict whether or not you’re going to default. In short, sequences are everywhere, and being able to analyze them is an important skill in your data science toolbox.

The easiest way to appreciate the kind of information you get from a sequence is to consider what you are reading right now. If I had written the previous sentence backwards, it wouldn’t make much sense to you, even though it contained all the same words. So order is important.

While the current fad in deep learning is to use recurrent neural networks to model sequences, I want to first introduce you guys to a machine learning algorithm that has been around for several decades now - the Hidden Markov Model.

This course follows directly from my first course in Unsupervised Machine Learning for Cluster Analysis, where you learned how to measure the probability distribution of a random variable. In this course, you’ll learn to measure the probability distribution of a sequence of random variables.

You guys know how much I love deep learning, so there is a little twist in this course. We’ve already covered gradient descent and you know how central it is for solving deep learning problems. I claimed that gradient descent could be used to optimize any objective function. In this course I will show you how you can use gradient descent to solve for the optimal parameters of an HMM, as an alternative to the popular expectation-maximization algorithm.

We’re going to do it in Theano and Tensorflow, which are popular libraries for deep learning. This is also going to teach you how to work with sequences in Theano and Tensorflow, which will be very useful when we cover recurrent neural networks and LSTMs.

This course is also going to go through the many practical applications of Markov models and hidden Markov models. We’re going to look at a model of sickness and health, and calculate how to predict how long you’ll stay sick, if you get sick. We’re going to talk about how Markov models can be used to analyze how people interact with your website, and fix problem areas like high bounce rate, which could be affecting your SEO. We’ll build language models that can be used to identify a writer and even generate text - imagine a machine doing your writing for you. HMMs have been very successful in natural language processing or NLP.

We’ll look at what is possibly the most recent and prolific application of Markov models - Google’s PageRank algorithm. And finally we’ll discuss even more practical applications of Markov models, including generating images, smartphone autosuggestions, and using HMMs to answer one of the most fundamental questions in biology - how is DNA, the code of life, translated into physical or behavioral attributes of an organism?

All of the materials of this course can be downloaded and installed for FREE. We will do most of our work in Numpy and Matplotlib, along with a little bit of Theano. I am always available to answer your questions and help you along your data science journey.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

See you in class!

Suggested Prerequisites:

  • calculus

  • linear algebra

  • probability

  • Be comfortable with the multivariate Gaussian distribution

  • Python coding: if/else, loops, lists, dicts, sets

  • Numpy coding: matrix and vector operations, loading a CSV file

TIPS (for getting through the course):

  • Watch it at 2x.

  • Take handwritten notes. This will drastically increase your ability to retain the information.

  • Write down the equations. If you don't, I guarantee it will just look like gibberish.

  • Ask lots of questions on the discussion board. The more the better!

  • Realize that most exercises will take you days or weeks to complete.

  • Write code yourself, don't just sit there and look at my code.


  • Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)

Who this course is for:
  • Students and professionals who do data analysis, especially on sequence data
  • Professionals who want to optimize their website experience
  • Students who want to strengthen their machine learning knowledge and practical skillset
  • Students and professionals interested in DNA analysis and gene expression
  • Students and professionals interested in modeling language and generating text from a model