Online Course DownloadOnline Course Download
    Facebook Twitter Instagram
    Online Course Download Online Course Download
    • Home
    • About Us
    • Contact Us
    • Privacy Policy
    Online Course DownloadOnline Course Download
    You are at:Home»Development»Unsupervised Machine Learning Hidden Markov Models in Python
    Development Data Science

    Unsupervised Machine Learning Hidden Markov Models in Python

    Facebook Twitter Pinterest LinkedIn Tumblr Email
    Unsupervised Machine Learning Hidden Markov Models in Python - Online Course Download
    Unsupervised Machine Learning Hidden Markov Models in Python - Online Course Download
    Share
    Facebook Twitter LinkedIn Pinterest Email
    Data Science Development free online course free udemy paid course freecourse freecoursesite Python udemy course download udemy courses free download

    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!

     

     

    Suggested Prerequisites:

    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

     

    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

    Course Size Details :

    9.5 hours on-demand video
    Full lifetime access
    Access on mobile and TV
    Certificate of completion

    View Demo

    People also Search on Google

    • free course download
    • download udemy courses on pc
    • udemy courses free download google drive
    • udemy courses free download
    • udemy online courses
    • online course download
    • udemy course download
    • udemy paid course for free
    • freecousesite
    • download udemy paid courses for free


    Online Course Download Unsupervised Machine Learning Hidden Markov Models in Python
    Share. Facebook Twitter Pinterest LinkedIn Tumblr Email
    Previous ArticleDocker and Containers Essentials
    Next Article DIY Website Design Bootcamp: WordPress Elementor Plugin

    Related Posts

    freecoursesite

    freecoursesite – Free Course Site

    Business

    PMP Exam Cram Session – PMBOK Guide, 6th Edition

    Business

    Operations Management

    Leave A Reply Cancel Reply

    • Strategies for Digital Marketers on Digital Marketing – 2023
    • Strategies for Digital Marketers on Digital Marketing – 2022
    • Digital Marketing Services To Transform Your Business Globally
    • freecoursesite – Free Course Site
    • What Every Business Owner Must Know About Digital Marketing
    • Facebook
    • Twitter
    • Instagram
    • Pinterest
    Don't Miss

    Strategies for Digital Marketers on Digital Marketing – 2023

    Strategies for Digital Marketers on Digital Marketing – 2022

    Digital Marketing Services To Transform Your Business Globally

    freecoursesite – Free Course Site

    About Us
    About Us

    Download Courses for Free. Learn web development, Programming, IT & Software, Marketing, Music, Free Online Courses, and more. freecoursesite

    USEFUL LINKS

    • Home
    • About Us
    • Contact Us
    • Privacy Policy
    • Sitemap
    • freecoursesite
    • freecoursesite
    Popular Posts
    Business

    Strategies for Digital Marketers on Digital Marketing – 2023

    Business

    Strategies for Digital Marketers on Digital Marketing – 2022

    Business

    Digital Marketing Services To Transform Your Business Globally

    Copyright © 2023 Online Course Download. All Rights Reserved.
    • Home
    • About Us
    • Contact Us
    • Privacy Policy
    • Sitemap
    • freecoursesite
    • freecoursesite

    Type above and press Enter to search. Press Esc to cancel.