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!
All the code for this course can be downloaded from my github: /lazyprogrammer/machine_learning_examples
In the directory: hmm_class
Make sure you always “git pull” so you have the latest version!
HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:
- linear algebra
- 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.
USEFUL COURSE ORDERING:
- (The Numpy Stack in Python)
- Linear Regression in Python
- Logistic Regression in Python
- (Supervised Machine Learning in Python)
- (Bayesian Machine Learning in Python: A/B Testing)
- Deep Learning in Python
- Practical Deep Learning in Theano and TensorFlow
- (Supervised Machine Learning in Python 2: Ensemble Methods)
- Convolutional Neural Networks in Python
- (Easy NLP)
- (Cluster Analysis and Unsupervised Machine Learning)
- Unsupervised Deep Learning
- (Hidden Markov Models)
- Recurrent Neural Networks in Python
- Artificial Intelligence: Reinforcement Learning in Python
- Natural Language Processing with Deep Learning in Python
Who is the target audience?
- 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
Created by Lazy Programmer Inc.
Last updated 8/2017
Size: 710.40 MB