Lazy Programmer Inc.
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.
Instructor Details
Courses : 22
Specification: Unsupervised Machine Learning Hidden Markov Models in Python
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7 reviews for Unsupervised Machine Learning Hidden Markov Models in Python
Add a review
| Price | $12.99 |
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| Provider | |
| Duration | 9 hours |
| Year | 2020 |
| Level | All |
| Language | English ... |
| Certificate | Yes |
| Quizzes | No |
$12.99
Steve –
So far, so good. A good textbook reference to follow along with or pre read would be helpful. But the coding examples, along with the code respository, have been good so far.
krish J –
very good course
Jos ngel Hermosillo G mez –
There are a lot of vagueness in the explanations, the slides do not show the important facts. The problems to solve are not well explained and that implies not to figure out a properly well defined solution. There are more high quality free material in internet than this.
Vidhya Chandrasekaran –
Very in depth content
Arun Kumar Kumirishetty –
Started with the course and I’m liking it as I hardly worked on unsupervised models
Deepak Khandelwal –
good
Josip Lazarevski –
The style of lecturing/ the git hub content managing is really poor. Maybe he has knowledge of the topic but he needs to polish how he is managing and transferring its knowledge.