Unsupervised Machine Learning Hidden Markov Models in Python

[0dayddl]

Unsupervised Machine Learning Hidden Markov Models in Python
.MP4 | Video: 1280x720, 30 fps(r) | Audio: AAC, 48000 Hz, 2ch | 2.90 GB
Duration: 7.5 hours | Genre: eLearning Video | Language: English

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.

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.

See you in class!

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

calculus

linear algebra

probability

Be comfortable with the multivariate Gaussian distribution

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations, loading a CSV file

Cluster Analysis and Unsupervised Machine Learning in Python will provide you with sufficient background

TIPS (for getting through the course):

Watch it at 2x.

Take handwritten notes. This will drastically increase your ability to retain the information.

Write down the equations. If you don't, I guarantee it will just look like gibberish.

Ask lots of questions on the discussion board. The more the better!

Realize that most exercises will take you days or weeks to complete.

Write code yourself, don't just sit there and look at my code.

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)

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

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[0dayddl]

1.91 GB | 20min 20s | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English​

Files Included :

Spoiler

001 Introduction and Outline Why would you want to use an HMM.mp4 (34.15 MB)
002 Where to get the Code and Data.mp4 (73.24 MB)
003 How to Succeed in this Course.mp4 (16.22 MB)
001 The Markov Property.mp4 (32.25 MB)
002 Markov Models.mp4 (45.85 MB)
003 Probability Smoothing and Log-Space.mp4 (34.04 MB)
004 The Math of Markov Chains.mp4 (58.93 MB)
001 Example Problem Sick or Healthy.mp4 (5.14 MB)
002 Example Problem Expected number of continuously sick days.mp4 (4.3 MB)
003 Example application SEO and Bounce Rate Optimization.mp4 (29.73 MB)
004 Example Application Build a 2nd-order language model and generate phrases.mp4 (29.61 MB)
005 Example Application Google's PageRank algorithm.mp4 (12.35 MB)
006 Suggestion Box.mp4 (27.17 MB)
001 From Markov Models to Hidden Markov Models.mp4 (11.59 MB)
002 HMM - Basic Examples.mp4 (15.04 MB)
003 Parameters of an HMM.mp4 (12.68 MB)
004 The 3 Problems of an HMM.mp4 (10.29 MB)
005 The Forward-Backward Algorithm (part 1).mp4 (26.01 MB)
006 The Forward-Backward Algorithm (part 2).mp4 (11.28 MB)
007 The Forward-Backward Algorithm (part 3).mp4 (12.06 MB)
008 The Viterbi Algorithm (part 1).mp4 (10.5 MB)
009 The Viterbi Algorithm (part 2).mp4 (24.01 MB)
010 HMM Training (part 1).mp4 (8.07 MB)
011 HMM Training (part 2).mp4 (15.92 MB)
012 HMM Training (part 3).mp4 (27.29 MB)
013 HMM Training (part 4).mp4 (21.84 MB)
014 How to Choose the Number of Hidden States.mp4 (13.32 MB)
015 Baum-Welch Updates for Multiple Observations.mp4 (8.91 MB)
016 Discrete HMM in Code.mp4 (69.48 MB)
017 The underflow problem and how to solve it.mp4 (9.47 MB)
018 Discrete HMM Updates in Code with Scaling.mp4 (34.28 MB)
019 Scaled Viterbi Algorithm in Log Space.mp4 (10.93 MB)
001 Gradient Descent Tutorial.mp4 (15.19 MB)
002 Theano Scan Tutorial.mp4 (33.63 MB)
003 Discrete HMM in Theano.mp4 (33.69 MB)
004 Improving our Gradient Descent-Based HMM.mp4 (10.08 MB)
005 Tensorflow Scan Tutorial.mp4 (41.48 MB)
006 Discrete HMM in Tensorflow.mp4 (50.58 MB)
001 Gaussian Mixture Models with Hidden Markov Models.mp4 (7.18 MB)
002 Generating Data from a Real-Valued HMM.mp4 (20.95 MB)
003 Continuous-Observation HMM in Code (part 1).mp4 (54.3 MB)
004 Continuous-Observation HMM in Code (part 2).mp4 (41.68 MB)
005 Continuous HMM in Theano.mp4 (64.28 MB)
006 Continuous HMM in Tensorflow.mp4 (65.25 MB)
001 Unsupervised or Supervised.mp4 (6.72 MB)
002 Generative vs Discriminative Classifiers.mp4 (5.31 MB)
003 HMM Classification on Poetry Data (Robert Frost vs Edgar Allan Poe).mp4 (34.72 MB)
001 Parts-of-Speech Tagging Concepts.mp4 (9.69 MB)
002 POS Tagging with an HMM.mp4 (15.38 MB)
001 (Review) Gaussian Mixture Models.mp4 (4.64 MB)
002 (Review) Theano Tutorial.mp4 (40.74 MB)
003 (Review) Tensorflow Tutorial.mp4 (23.33 MB)
001 Pre-Installation Check.mp4 (8.92 MB)
002 Anaconda Environment Setup.mp4 (168.03 MB)
003 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 (109.17 MB)
001 How to Code by Yourself (part 1).mp4 (56.12 MB)
002 How to Code by Yourself (part 2).mp4 (20.87 MB)
003 Proof that using Jupyter Notebook is the same as not using it.mp4 (37.08 MB)
004 Python 2 vs Python 3.mp4 (7.61 MB)
001 How to Succeed in this Course (Long Version).mp4 (17.87 MB)
002 Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 (42.45 MB)
003 Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 (27.51 MB)
004 Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 (81.2 MB)
001 What is the Appendix.mp4 (6.15 MB)
002 BONUS.mp4 (39.93 MB)

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