| 1. (Review) Gaussian Mixture Models.mp4 | 5 MB | ||
| 1. (Review) Gaussian Mixture Models.vtt | 3.3 KB | ||
| 1. Example Problem Sick or Healthy.mp4 | 5.5 MB | ||
| 1. Example Problem Sick or Healthy.vtt | 4.3 KB | ||
| 1. From Markov Models to Hidden Markov Models.mp4 | 10.2 MB | ||
| 1. From Markov Models to Hidden Markov Models.vtt | 7.9 KB | ||
| 1. Gaussian Mixture Models with Hidden Markov Models.mp4 | 16.5 MB | ||
| 1. Gaussian Mixture Models with Hidden Markov Models.vtt | 4.9 KB | ||
| 1. Generative vs. Discriminative Classifiers.mp4 | 4.1 MB | ||
| 1. Generative vs. Discriminative Classifiers.vtt | 3.3 KB | ||
| 1. Gradient Descent Tutorial.mp4 | 22.8 MB | ||
| 1. Gradient Descent Tutorial.vtt | 5.2 KB | ||
| 1. Introduction and Outline Why would you want to use an HMM.mp4 | 6.8 MB | ||
| 1. Introduction and Outline Why would you want to use an HMM.vtt | 5.4 KB | ||
| 1. Parts-of-Speech Tagging Concepts.mp4 | 8.5 MB | ||
| 1. Parts-of-Speech Tagging Concepts.vtt | 6.3 KB | ||
| 1. The Markov Property.mp4 | 8.3 MB | ||
| 1. The Markov Property.vtt | 6.2 KB | ||
| 1. What is the Appendix.mp4 | 5.5 MB | ||
| 1. What is the Appendix.vtt | 3.4 KB | ||
| 10. Baum-Welch Updates for Multiple Observations.mp4 | 7.5 MB | ||
| 10. Baum-Welch Updates for Multiple Observations.vtt | 5.6 KB | ||
| 10. What order should I take your courses in (part 1).mp4 | 29.3 MB | ||
| 10. What order should I take your courses in (part 1).vtt | 15.2 KB | ||
| 11. Discrete HMM in Code.mp4 | 47.4 MB | ||
| 11. Discrete HMM in Code.vtt | 13.5 KB | ||
| 11. What order should I take your courses in (part 2).mp4 | 37.6 MB | ||
| 11. What order should I take your courses in (part 2).vtt | 22.3 KB | ||
| 12. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 | 4 MB | ||
| 12. BONUS Where to get Udemy coupons and FREE deep learning material.vtt | 3.3 KB | ||
| 12. The underflow problem and how to solve it.mp4 | 7.7 MB | ||
| 12. The underflow problem and how to solve it.vtt | 6.1 KB | ||
| 13. Discrete HMM Updates in Code with Scaling.mp4 | 29.1 MB | ||
| 13. Discrete HMM Updates in Code with Scaling.vtt | 7.8 KB | ||
| 14. Scaled Viterbi Algorithm in Log Space.mp4 | 9.2 MB | ||
| 14. Scaled Viterbi Algorithm in Log Space.vtt | 2.4 KB | ||
| 2. (Review) Theano Tutorial.mp4 | 19.9 MB | ||
| 2. (Review) Theano Tutorial.vtt | 7.1 KB | ||
| 2. Example Problem Expected number of continuously sick days.mp4 | 4.6 MB | ||
| 2. Example Problem Expected number of continuously sick days.vtt | 3.3 KB | ||
| 2. Generating Data from a Real-Valued HMM.mp4 | 14.9 MB | ||
| 2. Generating Data from a Real-Valued HMM.vtt | 4 KB | ||
| 2. HMM Classification on Poetry Data (Robert Frost vs. Edgar Allan Poe).mp4 | 24.4 MB | ||
| 2. HMM Classification on Poetry Data (Robert Frost vs. Edgar Allan Poe).vtt | 7.8 KB | ||
| 2. HMMs are Doubly Embedded.mp4 | 1.5 MB | ||
| 2. HMMs are Doubly Embedded.vtt | 2.6 KB | ||
| 2. Markov Models.mp4 | 8.2 MB | ||
| 2. Markov Models.vtt | 5.9 KB | ||
| 2. POS Tagging with an HMM.mp4 | 14.4 MB | ||
| 2. POS Tagging with an HMM.vtt | 4.6 KB | ||
| 2. Theano Scan Tutorial.mp4 | 23.8 MB | ||
| 2. Theano Scan Tutorial.vtt | 11.3 KB | ||
| 2. Unsupervised or Supervised.mp4 | 5.3 MB | ||
| 2. Unsupervised or Supervised.vtt | 3.7 KB | ||
| 2. Windows-Focused Environment Setup 2018.mp4 | 186.3 MB | ||
| 2. Windows-Focused Environment Setup 2018.vtt | 18.7 KB | ||
| 3. (Review) Tensorflow Tutorial.mp4 | 13.9 MB | ||
| 3. (Review) Tensorflow Tutorial.vtt | 5.6 KB | ||
| 3. Continuous-Observation HMM in Code (part 1).mp4 | 46.7 MB | ||
| 3. Continuous-Observation HMM in Code (part 1).vtt | 11.3 KB | ||
| 3. Discrete HMM in Theano.mp4 | 30.7 MB | ||
| 3. Discrete HMM in Theano.vtt | 7.4 KB | ||
| 3. Example application SEO and Bounce Rate Optimization.mp4 | 15.8 MB | ||
| 3. Example application SEO and Bounce Rate Optimization.vtt | 9.5 KB | ||
| 3. How can we choose the number of hidden states.mp4 | 7.3 MB | ||
| 3. How can we choose the number of hidden states.vtt | 5.6 KB | ||
| 3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 | 43.9 MB | ||
| 3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt | 13.4 KB | ||
| 3. The Math of Markov Chains.mp4 | 9 MB | ||
| 3. The Math of Markov Chains.vtt | 6.7 KB | ||
| 3. Where to get the Code and Data.mp4 | 2.1 MB | ||
| 3. Where to get the Code and Data.vtt | 1.7 KB | ||
| 4. Continuous-Observation HMM in Code (part 2).mp4 | 15.3 MB | ||
| 4. Continuous-Observation HMM in Code (part 2).vtt | 2.9 KB | ||
| 4. Example Application Build a 2nd-order language model and generate phrases.mp4 | 26.9 MB | ||
| 4. Example Application Build a 2nd-order language model and generate phrases.vtt | 12.3 KB | ||
| 4. How to Code by Yourself (part 1).mp4 | 24.5 MB | ||
| 4. How to Code by Yourself (part 1).vtt | 21.3 KB | ||
| 4. How to Succeed in this Course.mp4 | 3.3 MB | ||
| 4. How to Succeed in this Course.vtt | 3.7 KB | ||
| 4. Improving our Gradient Descent-Based HMM.mp4 | 25.9 MB | ||
| 4. Improving our Gradient Descent-Based HMM.vtt | 5.9 KB | ||
| 4. The Forward-Backward Algorithm.mp4 | 22.4 MB | ||
| 4. The Forward-Backward Algorithm.vtt | 5.2 KB | ||
| 5. Continuous HMM in Theano.mp4 | 45.4 MB | ||
| 5. Continuous HMM in Theano.vtt | 10.4 KB | ||
| 5. Example Application Google’s PageRank algorithm.mp4 | 8.7 MB | ||
| 5. Example Application Google’s PageRank algorithm.vtt | 6.5 KB | ||
| 5. How to Code by Yourself (part 2).mp4 | 14.8 MB | ||
| 5. How to Code by Yourself (part 2).vtt | 12.4 KB | ||
| 5. Tensorflow Scan Tutorial.mp4 | 23.1 MB | ||
| 5. Tensorflow Scan Tutorial.vtt | 14 KB | ||
| 5. Visual Intuition for the Forward Algorithm.mp4 | 6 MB | ||
| 5. Visual Intuition for the Forward Algorithm.vtt | 4.5 KB | ||
| 6. Continuous HMM in Tensorflow.mp4 | 22.5 MB | ||
| 6. Continuous HMM in Tensorflow.vtt | 10.4 KB | ||
| 6. Discrete HMM in Tensorflow.mp4 | 16.4 MB | ||
| 6. Discrete HMM in Tensorflow.vtt | 8.4 KB | ||
| 6. How to Succeed in this Course (Long Version).mp4 | 18.3 MB | ||
| 6. How to Succeed in this Course (Long Version).vtt | 13.7 KB | ||
| 6. The Viterbi Algorithm.mp4 | 5 MB | ||
| 6. The Viterbi Algorithm.vtt | 3.5 KB | ||
| 7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 | 39 MB | ||
| 7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt | 29.9 KB | ||
| 7. Visual Intuition for the Viterbi Algorithm.mp4 | 15.7 MB | ||
| 7. Visual Intuition for the Viterbi Algorithm.vtt | 3.9 KB | ||
| 8. Proof that using Jupyter Notebook is the same as not using it.mp4 | 78.3 MB | ||
| 8. Proof that using Jupyter Notebook is the same as not using it.vtt | 13.2 KB | ||
| 8. The Baum-Welch Algorithm.mp4 | 4.3 MB | ||
| 8. The Baum-Welch Algorithm.vtt | 3 KB | ||
| 9. Baum-Welch Explanation and Intuition.mp4 | 12 MB | ||
| 9. Baum-Welch Explanation and Intuition.vtt | 8.1 KB | ||
| 9. Python 2 vs Python 3.mp4 | 7.8 MB | ||
| 9. Python 2 vs Python 3.vtt | 5.9 KB | ||
| Readme.txt | 921.6 B | ||
| [GigaCourse.com].url | 0 B | ||
| ▲ 116 total files | |||
Udemy - Unsupervised Machine Learning Hidden Markov Models in Python
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. The easiest way to appreciate the kind of information you get from a sequence is to consider what you are reading right now. If I had written the previous sentence backwards, it wouldn’t make much sense to you, even though it contained all the same words. So order is important.
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