Udemy - Unsupervised Machine Learning Hidden Markov Models in Python (Updated 12 - 2020)

[ FreeCourseWeb.com ] Unsupervised Machine Learning Hidden Markov Models in Python (Updated 12/2020)



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MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch

Genre: eLearning | Language: English + .srt | Duration: 63 lectures (9h 13m) | Size: 1.38 GB

HMMs for stock price analysis, language modeling, web analytics, biology, and PageRank.

What you'll learn:

Understand and enumerate the various applications of Markov Models and Hidden Markov Models

Understand how Markov Models work

Write a Markov Model in code

Apply Markov Models to any sequence of data

Understand the mathematics behind Markov chains

Apply Markov models to language

Apply Markov models to website analytics

Understand how Google's PageRank works

Understand Hidden Markov Models

Write a Hidden Markov Model in Code

Write a Hidden Markov Model using Theano

Understand how gradient descent, which is normally used in deep learning, can be used for HMMs



Requirements

Familiarity with probability and statistics

Understand Gaussian mixture models

Be comfortable with Python and Numpy



Description

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.



Use Winrar to Extract. And use a shorter path when extracting, such as C: drive



ALSO ANOTHER TIP: You Can Easily Navigate Using Winrar and Rename the Too Long File/ Folder Name if Needed While You Cannot in Default Windows Explorer. You are Welcome ! :)



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