Description
Unsupervised Machine Learning Hidden Markov Models in Python is a course from Udemy that explains the Hidden Markov Model for Stock Price Analysis, Language Modeling, Website Statistics, and Biology. The hidden Markov model is generally related to sequences. A lot of data that is suitable for building the model is included in the sequence. For example, stock value is a sequence of prices and language is a sequence of words. In short, sequelae 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. Meanwhile, we work with Theano and Tensorflow libraries and explain the hidden Markov model in detail. This course examines many Markov models and the Hidden Markov Model, as well as how to analyze and predict patterns of disease and health.
Courses taught in this course:
- Familiarity with the different programs of the hidden Markov model
- Understand how the Markov model works
- Write code for a Markov model
- Apply the Markov model to a sequence of data
- Applying the Markov model to a language
- Writing the Hidden Markov Model with Theano
Specification of Unsupervised Machine Learning Hidden Markov Models in Python:
- English language
- Duration: 9 hours and 13 minutes
- Number of courses: 63
- Level of education: Average
- Instructor: Lazy Programmer Inc.
- File format: mp4
Course content
Course prerequisites
- Knowledge of probability and statistics
- Understanding Gaussian Mixture Models
- Be comfortable with Python and Numpy
Pictures
sample movie
Installation guide
After ripping, watch with your favorite player.
english subtitle
Quality: 720p
Changes:
The 2020/12 version compared to 2018/10 increased the number by 1 lesson and the duration by 12 minutes.
Download link
File password(s): ngaur.com
Cut
1.46 GB