Unsupervised Machine Learning Hidden Markov Models in Python. HMMs for stock price analysis, language modeling, web analytics, biology, and PageRank.
The Hidden Markov Model
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.
Unsupervised Machine Learning Hidden Markov Models in Python Description
Time Series Analysis in Python 2020
Familiarity with probability and statistics
Understand Gaussian mixture models
Be comfortable with Python and Numpy
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
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
Unsupervised Machine Learning Hidden Markov Models in Python Free Download
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Content From: https://www.udemy.com/course/unsupervised-machine-learning-hidden-markov-models-in-python/