[ FreeCourseWeb ] Udemy - Unsupervised Machine Learning Hidden Markov Models in Python (Updated 12 - 2020)
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- TypeTutorials
- LanguageEnglish
- Total size1.6 GB
- Uploaded Byfreecoursewb
- Downloads109
- Last checkedDec. 29th '20
- Date uploadedDec. 27th '20
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[ 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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