Unsupervised Machine Learning Hidden Markov Models in Python

Name: Unsupervised Machine Learning Hidden Markov Models in Python
Brand: Lazy Programmer
Price: 120.00 USD
Availability: Discontinued

4.5 Hours

$120.00

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40 Lessons (4.5h)

Introduction and Outline
Introduction and Outline: Why would you want to use an HMM?4:04
Unsupervised or Supervised?2:58
Markov Models
The Markov Property4:39
Markov Models4:50
The Math of Markov Chains5:13
Markov Models: Example Problems and Applications
Example Problem: Sick or Healthy3:26
Example Problem: Expected number of continuously sick days2:53
Example application: SEO and Bounce Rate Optimization8:53
Example Application: Build a 2nd-order language model and generate phrases13:06
Example Application: Google’s PageRank algorithm5:04
Hidden Markov Models for Discrete Observations
From Markov Models to Hidden Markov Models6:02
HMMs are Doubly Embedded1:59
How can we choose the number of hidden states?4:22
The Forward-Backward Algorithm4:27
Visual Intuition for the Forward Algorithm3:32
The Viterbi Algorithm2:57
Visual Intuition for the Viterbi Algorithm3:16
The Baum-Welch Algorithm2:38
Baum-Welch Explanation and Intuition6:34
Baum-Welch Updates for Multiple Observations4:53
Discrete HMM in Code20:33
The underflow problem and how to solve it5:05
Discrete HMM Updates in Code with Scaling11:53
Scaled Viterbi Algorithm in Log Space3:38
Gradient Descent Tutorial4:30
Theano Scan Tutorial12:40
Discrete HMM in Theano11:42
HMMs for Continuous Observations
Gaussian Mixture Models with Hidden Markov Models4:12
Generating Data from a Real-Valued HMM6:35
Continuous-Observation HMM in Code (part 1)18:38
Continuous-Observation HMM in Code (part 2)5:12
Continuous HMM in Theano16:32
HMMs for Classification
Generative vs. Discriminative Classifiers2:30
HMM Classification on Poetry Data (Robert Frost vs. Edgar Allan Poe)10:36
Bonus Example: Parts-of-Speech Tagging
Parts-of-Speech Tagging Concepts5:00
POS Tagging with an HMM5:58
Appendix
Review of Gaussian Mixture Models3:04
Theano Tutorial7:47
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow17:22

DescriptionInstructorImportant DetailsRelated Products

Decode & Analyze Important Data Sequences & Solve Everyday Problems

Lazy ProgrammerThe Lazy Programmer is a data scientist, big data engineer, and full stack software engineer. For his master's thesis he worked on brain-computer interfaces using machine learning. These assist non-verbal and non-mobile persons to communicate with their family and caregivers.

He has worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. He has created new big data pipelines using Hadoop/Pig/MapReduce, and created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.

He has taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School.

Multiple businesses have benefitted from his web programming expertise. He does all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies he has used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases he has used MySQL, Postgres, Redis, MongoDB, and more.

Description

Data, in many forms, is presented in sequences: stock prices, language, credit scoring, etc. Being able to analyze them, therefore, is of invaluable importance. In this course you'll learn a machine learning algorithm - the Hidden Markov Model - to model sequences effectively. You'll also delve deeper into the many practical applications of Markov Models and Hidden Markov Models.

Access 40 lectures & 4.5 hours of content 24/7
Use gradient descent to solve for the optimal parameters of a Hidden Markov Model
Learn how to work w/ sequences in Theano
Calculate models of sickness & health
Analyze how people interact w/ a website using Markov Models
Explore Google's PageRank algorithm
Generate images & discuss smartphone autosuggestions using HMMs

Think this is cool? Check out the other bundles in this series, The Deep Learning and Artificial Intelligence Introductory Bundle, and The Advanced Guide to Deep Learning and Artificial Intelligence.

Specs

Details & Requirements

Length of time users can access this course: lifetime
Access options: web streaming, mobile streaming
Certification of completion not included
Redemption deadline: redeem your code within 30 days of purchase
Experience level required: all levels, but knowledge of Python and Numpy coding is expected
All code for this course is available for download here, in the directory hmm_class

Terms

Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.

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