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Unsupervised Machine Learning: Hidden Markov Models in Python
Introduction and Outline
Introduction and Outline: Why would you want to use an HMM? (5:05)
Special Offer! Get the VIP version of this course (1:14)
Unsupervised or Supervised? (2:58)
Where to get the Code and Data (1:20)
How to Succeed in this Course (3:13)
Markov Models
The Markov Property (4:39)
Markov Models (4:50)
The Math of Markov Chains (5:13)
Markov Models: Example Problems and Applications
Example Problem: Sick or Healthy (3:26)
Example Problem: Expected number of continuously sick days (2:53)
Example application: SEO and Bounce Rate Optimization (8:53)
Example Application: Build a 2nd-order language model and generate phrases (13:06)
Example Application: Google’s PageRank algorithm (5:04)
Hidden Markov Models for Discrete Observations
From Markov Models to Hidden Markov Models (6:02)
HMMs are Doubly Embedded (1:59)
How can we choose the number of hidden states? (4:22)
The Forward-Backward Algorithm (4:27)
Visual Intuition for the Forward Algorithm (3:32)
The Viterbi Algorithm (2:57)
Visual Intuition for the Viterbi Algorithm (3:16)
The Baum-Welch Algorithm (2:38)
Baum-Welch Explanation and Intuition (6:34)
Baum-Welch Updates for Multiple Observations (4:53)
Discrete HMM in Code (20:33)
The underflow problem and how to solve it (5:05)
Discrete HMM Updates in Code with Scaling (11:53)
Scaled Viterbi Algorithm in Log Space (3:38)
Gradient Descent Tutorial (4:30)
Theano Scan Tutorial (12:40)
Discrete HMM in Theano (11:42)
HMMs for Continuous Observations
Gaussian Mixture Models with Hidden Markov Models (4:12)
Generating Data from a Real-Valued HMM (6:35)
Continuous-Observation HMM in Code (part 1) (18:37)
Continuous-Observation HMM in Code (part 2) (5:12)
Continuous HMM in Theano (16:32)
HMMs for Classification
Generative vs. Discriminative Classifiers (2:30)
HMM Classification on Poetry Data (Robert Frost vs. Edgar Allan Poe) (10:36)
Bonus Example: Parts-of-Speech Tagging
Parts-of-Speech Tagging Concepts (5:00)
POS Tagging with an HMM (5:58)
Appendix
Review of Gaussian Mixture Models (3:04)
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:22)
How to Code by Yourself (part 1) (15:54)
How to Code by Yourself (part 2) (9:23)
How to Succeed in this Course (Long Version) (10:24)
BONUS: Where to get discount coupons and FREE deep learning material (5:31)
Example Application: Google’s PageRank algorithm
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