English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 65 Lessons (9h 48m) | 2.80 GB
HMMs for stock price analysis, language modeling, web analytics, biology, and PageRank.
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.
While the current fad in deep learning is to use recurrent neural networks to model sequences, I want to first introduce you guys to a machine learning algorithm that has been around for several decades now – the Hidden Markov Model.
This course follows directly from my first course in Unsupervised Machine Learning for Cluster Analysis, where you learned how to measure the probability distribution of a random variable. In this course, you’ll learn to measure the probability distribution of a sequence of random variables.
You guys know how much I love deep learning, so there is a little twist in this course. We’ve already covered gradient descent and you know how central it is for solving deep learning problems. I claimed that gradient descent could be used to optimize any objective function. In this course I will show you how you can use gradient descent to solve for the optimal parameters of an HMM, as an alternative to the popular expectation-maximization algorithm.
We’re going to do it in Theano and Tensorflow, which are popular libraries for deep learning. This is also going to teach you how to work with sequences in Theano and Tensorflow, which will be very useful when we cover recurrent neural networks and LSTMs.
This course is also going to go through the many practical applications of Markov models and hidden Markov models. We’re going to look at a model of sickness and health, and calculate how to predict how long you’ll stay sick, if you get sick. We’re going to talk about how Markov models can be used to analyze how people interact with your website, and fix problem areas like high bounce rate, which could be affecting your SEO. We’ll build language models that can be used to identify a writer and even generate text – imagine a machine doing your writing for you. HMMs have been very successful in natural language processing or NLP.
We’ll look at what is possibly the most recent and prolific application of Markov models – Google’s PageRank algorithm. And finally we’ll discuss even more practical applications of Markov models, including generating images, smartphone autosuggestions, and using HMMs to answer one of the most fundamental questions in biology – how is DNA, the code of life, translated into physical or behavioral attributes of an organism?
All of the materials of this course can be downloaded and installed for FREE. We will do most of our work in Numpy and Matplotlib, along with a little bit of Theano. I am always available to answer your questions and help you along your data science journey.
This course focuses on “how to build and understand”, not just “how to use”. Anyone can learn to use an API in 15 minutes after reading some documentation. It’s not about “remembering facts”, it’s about “seeing for yourself” via experimentation. It will teach you how to visualize what’s happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.
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
Who this course is for:
- Students and professionals who do data analysis, especially on sequence data
- Professionals who want to optimize their website experience
- Students who want to strengthen their machine learning knowledge and practical skillset
- Students and professionals interested in DNA analysis and gene expression
- Students and professionals interested in modeling language and generating text from a model
Table of Contents
Introduction and Outline
1 Introduction and Outline – Why would you want to use an HMM
2 Where to get the Code and Data
3 How to Succeed in this Course
Markov Models
4 The Markov Property
5 Markov Models
6 Probability Smoothing and Log-Space
7 The Math of Markov Chains
Markov Models – Example Problems and Applications
8 Example Problem – Sick or Healthy
9 Example Problem – Expected number of continuously sick days
10 Example application – SEO and Bounce Rate Optimization
11 Example Application – Build a 2nd-order language model and generate phrases
12 Example Application – Google’s PageRank algorithm
13 Suggestion Box
Hidden Markov Models for Discrete Observations
14 From Markov Models to Hidden Markov Models
15 HMM – Basic Examples
16 Parameters of an HMM
17 The 3 Problems of an HMM
18 The Forward-Backward Algorithm (part 1)
19 The Forward-Backward Algorithm (part 2)
20 The Forward-Backward Algorithm (part 3)
21 The Viterbi Algorithm (part 1)
22 The Viterbi Algorithm (part 2)
23 HMM Training (part 1)
24 HMM Training (part 2)
25 HMM Training (part 3)
26 HMM Training (part 4)
27 How to Choose the Number of Hidden States
28 Baum-Welch Updates for Multiple Observations
29 Discrete HMM in Code
30 The underflow problem and how to solve it
31 Discrete HMM Updates in Code with Scaling
32 Scaled Viterbi Algorithm in Log Space
Discrete HMMs Using Deep Learning Libraries
33 Gradient Descent Tutorial
34 Theano Scan Tutorial
35 Discrete HMM in Theano
36 Improving our Gradient Descent-Based HMM
37 Tensorflow Scan Tutorial
38 Discrete HMM in Tensorflow
HMMs for Continuous Observations
39 Gaussian Mixture Models with Hidden Markov Models
40 Generating Data from a Real-Valued HMM
41 Continuous-Observation HMM in Code (part 1)
42 Continuous-Observation HMM in Code (part 2)
43 Continuous HMM in Theano
44 Continuous HMM in Tensorflow
HMMs for Classification
45 Unsupervised or Supervised
46 Generative vs. Discriminative Classifiers
47 HMM Classification on Poetry Data (Robert Frost vs. Edgar Allan Poe)
Bonus Example – Parts-of-Speech Tagging
48 Parts-of-Speech Tagging Concepts
49 POS Tagging with an HMM
Theano, Tensorflow, and Machine Learning Basics Review
50 (Review) Gaussian Mixture Models
51 (Review) Theano Tutorial
52 (Review) Tensorflow Tutorial
Appendix – FAQ Finale
53 What is the Appendix
Setting Up Your Environment (FAQ by Student Request)
54 Pre-Installation Check
55 Anaconda Environment Setup
56 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
Extra Help With Python Coding for Beginners (FAQ by Student Request)
57 How to Code by Yourself (part 1)
58 How to Code by Yourself (part 2)
59 Proof that using Jupyter Notebook is the same as not using it
60 Python 2 vs Python 3
Effective Learning Strategies for Machine Learning (FAQ by Student Request)
61 How to Succeed in this Course (Long Version)
62 Is this for Beginners or Experts Academic or Practical Fast or slow-paced
63 Machine Learning and AI Prerequisite Roadmap (pt 1)
64 Machine Learning and AI Prerequisite Roadmap (pt 2)
Appendix – FAQ Finale
65 BONUS
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