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

#### What you’ll learn

Unsupervised Machine Learning Hidden Markov Models in Python Course

- 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

#### 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. 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. 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.

#### 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
- 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
- Last updated 12/2019
- Machine Learning Basics: Building Regression Model in Python

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Content From: https://www.udemy.com/course/unsupervised-machine-learning-hidden-markov-models-in-python/