Udemy – Unsupervised Machine Learning Hidden Markov Models in Python 2018-10

Description

Unsupervised Machine Learning Hidden Markov Models in Python is a course from the Udemy site that explains Markov's Hidden Markov model for stock price analysis, language modeling, website statistics, and biology. Markov's hidden model is generally related to sequences. Much of the data that is suitable for modeling is contained in sequences. For example, stock value is a sequence of prices and language is a sequence of words. In short, sequences are ubiquitous, and having the ability 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 in-depth learning. During this course we work with Theano and Tensorflow libraries and fully explain Markov's hidden model. This course examines many projects of the Markov model and the hidden Markov model, and also shows how to analyze and predict the disease and health model.

Items that will be taught in this course

Familiarity with various programs of the hidden Markov model

Understand how the Markov model works

Write the code of a Markov model

Apply the Markov model to a sequence of data

Apply Markov model on a language

Write Markov's hidden model using Theano

Course Details Unsupervised Machine Learning Hidden Markov Models in Python

English language

Duration: 9 hours and 1 minute

Number of courses: 62

Level of education: Intermediate

Instructor: Lazy Programmer Inc

File format: mp4

Unsupervised Machine Learning Hidden Markov Models in Python

62 lectures 09:01:50

Introduction and Outline 4 lectures 11:34

Markov Models 3 lectures 16:56

Markov Models: Example Problems and Applications 5 lectures 33:22

Hidden Markov Models for Discrete Observations 19 lectures 02:54:24

Discrete HMMs Using Deep Learning Libraries 6 lectures 54:10

HMMs for Continuous Observations 6 lectures 01:00:34

HMMs for Classification 2 lectures 13:06

Bonus Example: Parts-of-Speech Tagging 2 lectures 10:58

Basics Review 3 lectures 18:18

Appendix / FAQ 12 lectures 02:28:28

Prerequisite

Familiarity with probability and statistics

Understand Gaussian mixture models

Be comfortable with Python and Numpy

Installation

After Extract, watch with your favorite Player.

English subtitle

Quality: 720p