Udemy – Unsupervised Machine Learning Hidden Markov Models in Python 2023-11
Udemy – Unsupervised Machine Learning Hidden Markov Models in Python 2023-11 Downloadly IRSpace
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Unsupervised Machine Learning Hidden Markov Models in Python is a course from Udemy that explains Markov’s hidden model for stock price analysis, language modeling, website statistics, and biology. Markov’s hidden model is generally related to sequels. Many of the data that are suitable for building the model are included in the sequence. For example, stock value is a sequence of prices and language is a sequence of words. In short, sequels are everywhere, and having the ability to analyze them is an essential skill in data science.
In this tutorial, you will learn how to measure the probability distribution of a sequence of random variables and learn a lot about deep learning. During this time we work with Theano and Tensorflow libraries and fully explain the hidden model of Markov. This course examines many of Markov’s models and Markov’s hidden model, as well as how to analyze and predict disease and health models.
Courses taught in this course:
- Familiarity with the various programs of Markov’s hidden model
- Understand how the Markov model works
- Writing code for a Markov model
- Apply the Markov model on a data sequence
- Apply the Markov model on a language
- Writing the hidden model of Markov using Theano
Specifications of Unsupervised Machine Learning Hidden Markov Models in Python:
- Publisher: Udemy
- Instructor: Lazy Programmer Inc.
- English language
- Duration: 9 hours and 46 minute
- Number of lessons: 65
- Level of education: Medium
Course Content on 2024/2
Course prerequisites
- Familiarity with probability and statistics
- Understand Gaussian mixture models
- Be comfortable with Python and Numpy
Pictures
Sample movie
Installation guide
After Extract, view with your favorite Player.
Subtitle: English
Quality: 720p
Changes:
Version 2020/12 compared to 2018/10 has increased the number of 1 lesson and the duration of 12 minutes.
Version 2023/11 compared to 2020/12 has increased the number of 2 lessons and the duration of 33 minutes.
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Size
1.99 GB
