Unsupervised Machine Learning Hidden Markov Models in Python is a course from Udemy that explains hidden Markov models for stock price analysis, language modeling, website statistics, and biology. The hidden Markov model is generally related to sequences. Most of the data that are suitable for building the model are located in sequences.
For example, stock value is a sequence of prices and language is a sequence of words. In short, sequences are everywhere, and being able to analyze them is an essential skill in data science.
In this course, you will learn how to measure the probability distribution of a sequence of random variables and learn a lot about deep learning. During this course, we work with Theano and Tensorflow libraries and fully explain the hidden Markov model. This course examines many Markov Model and Hidden Markov Model projects and shows how to analyze and predict disease and health models.
Items taught in this course:
- Familiarity with different hidden Markov model programs
- Understanding how the Markov model works
- Write the code of a Markov model
- Applying a Markov model to a sequence of data
- Applying the Markov model to a language
- Writing a hidden Markov model using Theano
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Specifications of Unsupervised Machine Learning Hidden Markov Models in Python course:
- English language
- Duration: 9 hours and 13 minutes
- Number of courses: 63
- Education level: Intermediate
- Instructor: Lazy Programmer Inc
- File format: mp4
Unsupervised Machine Learning Course headings
Unsupervised Machine Learning Course prerequisites
- Familiarity with probability and statistics
- Understand Gaussian mixture models
- Be comfortable with Python and Numpy
After extracting, watch with your favorite player.
The 12/2020 version has increased by 1 lesson and the duration of 12 minutes compared to the 2018/10 version.
Download part 1 – 1 GB
Download part 2 – 467 MB
file password link
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