Unsupervised Machine Learning Hidden Markov Models in Python
4.6 (1,576 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.
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Unsupervised Machine Learning Hidden Markov Models in Python

HMMs for stock price analysis, language modeling, web analytics, biology, and PageRank.
4.6 (1,576 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.
14,530 students enrolled
Last updated 10/2018
English
English [Auto-generated], Portuguese [Auto-generated]
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This course includes
  • 7.5 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
Requirements
  • Familiarity with probability and statistics
  • Understand Gaussian mixture models
  • Be comfortable with Python and Numpy
Description

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!



HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

  • 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

  • Cluster Analysis and Unsupervised Machine Learning in Python will provide you with sufficient background


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)


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
Course content
Expand all 57 lectures 07:23:50
+ Markov Models
3 lectures 14:42
Markov Models
04:50
The Math of Markov Chains
05:13
+ Markov Models: Example Problems and Applications
5 lectures 33:22
Example Problem: Sick or Healthy
03:26
Example Problem: Expected number of continuously sick days
02:53
Example application: SEO and Bounce Rate Optimization
08:53
Example Application: Build a 2nd-order language model and generate phrases
13:06
Example Application: Google’s PageRank algorithm
05:04
+ Hidden Markov Models for Discrete Observations
14 lectures 01:21:49
From Markov Models to Hidden Markov Models
06:02
HMMs are Doubly Embedded
01:59
How can we choose the number of hidden states?
04:22
The Forward-Backward Algorithm
04:27
Visual Intuition for the Forward Algorithm
03:32
The Viterbi Algorithm
02:57
Visual Intuition for the Viterbi Algorithm
03:16
The Baum-Welch Algorithm
02:38
Baum-Welch Explanation and Intuition
06:34
Baum-Welch Updates for Multiple Observations
04:53
Discrete HMM in Code
20:33
The underflow problem and how to solve it
05:05
Discrete HMM Updates in Code with Scaling
11:53
Scaled Viterbi Algorithm in Log Space
03:38
+ Discrete HMMs Using Deep Learning Libraries
6 lectures 54:10
Gradient Descent Tutorial
04:30
Theano Scan Tutorial
12:40
Discrete HMM in Theano
11:42
Improving our Gradient Descent-Based HMM
05:09
Tensorflow Scan Tutorial
12:42
Discrete HMM in Tensorflow
07:27
+ HMMs for Continuous Observations
6 lectures 01:00:34
Gaussian Mixture Models with Hidden Markov Models
04:12
Generating Data from a Real-Valued HMM
06:35
Continuous-Observation HMM in Code (part 1)
18:37
Continuous-Observation HMM in Code (part 2)
05:12
Continuous HMM in Theano
16:32
Continuous HMM in Tensorflow
09:26
+ HMMs for Classification
2 lectures 13:06
Generative vs. Discriminative Classifiers
02:30
HMM Classification on Poetry Data (Robert Frost vs. Edgar Allan Poe)
10:36
+ Bonus Example: Parts-of-Speech Tagging
2 lectures 10:58

Note:

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
05:00
POS Tagging with an HMM
05:58
+ Basics Review
3 lectures 18:18
(Review) Gaussian Mixture Models
03:04
(Review) Theano Tutorial
07:47
(Review) Tensorflow Tutorial
07:27
+ Appendix
12 lectures 02:25:17
What is the Appendix?
02:48
Windows-Focused Environment Setup 2018
20:20
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
17:32
How to Code by Yourself (part 1)
15:54
How to Code by Yourself (part 2)
09:23
How to Succeed in this Course (Long Version)
10:24
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
22:04
Proof that using Jupyter Notebook is the same as not using it
12:29
Python 2 vs Python 3
04:38
What order should I take your courses in? (part 1)
11:18
What order should I take your courses in? (part 2)
16:07
BONUS: Where to get Udemy coupons and FREE deep learning material
02:20