[ 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.
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