# Unsupervised Machine Learning Hidden Markov Models in Python

4.7 (2690)
MOOC
Learning paid
English
9 hours course
Course by Udemy
\$ 77.99
\$ 77.99
What will you 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 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

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?

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

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.

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

• 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)

Program
Introduction and Outline
Introduction and Outline: Why would you want to use an HMM?
Unsupervised or Supervised?
Where to get the Code and Data
How to Succeed in this Course
Markov Models
The Markov Property
Markov Models
The Math of Markov Chains
Markov Models: Example Problems and Applications
Example Problem: Sick or Healthy
Example Problem: Expected number of continuously sick days
Requirements
• Familiarity with probability and statistics
• Understand Gaussian mixture models
• Be comfortable with Python and Numpy
Lecturers
Lazy Programmer Inc.
Artificial intelligence and machine learning engineer
Platform
Udemy courses are suited to professional development. The platform is organized in such a way that it is experts themselves that decide the topic and when the course will start. All supporting documents are made available to you for lifetime access. On this platform, you can find a course on about any subject, and that is no exaggeration – from a tutorial on how to ride a motorcycle, to managing the financial markets. The language and the course format are established by the teacher. This is why it is important to read the information about the course carefully before parting with any money.
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