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Unsupervised Machine Learning Hidden Markov Models in Python

$29.99 $12.00

4.88 (12 reviews)
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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

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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!

“If you can’t implement it, you don’t understand it”

  • Or as the great physicist Richard Feynman said: “What I cannot create, I do not understand”.

  • My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

  • Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

  • After doing the same thing with 10 datasets, you realize you didn’t learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times…

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

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

  • Check out the lecture “Machine Learning and AI Prerequisite Roadmap” (available in the FAQ 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 content

  • Introduction and Outline
  • Markov Models
  • Markov Models: Example Problems and Applications
  • Hidden Markov Models for Discrete Observations
  • Discrete HMMs Using Deep Learning Libraries
  • HMMs for Continuous Observations
  • HMMs for Classification
  • Bonus Example: Parts-of-Speech Tagging
  • Theano, Tensorflow, and Machine Learning Basics Review
  • Setting Up Your Environment (FAQ by Student Request)

12 reviews for Unsupervised Machine Learning Hidden Markov Models in Python

  1. Luis Brito

    I can definitely see this course helping breaking through that initial barrier when it comes to learning a programming language. I’m positive I’ll finish it and hopefully master python by then.

  2. Abdallah Galal Abouhussein

    I would like to share my personnel experience, i`m very pleased and happy for taking this impressive course because Dr. Angela always makes it simple and easy to understand and always makes a precaution section before or after any bug that may appear to some students and also write or record the solution for us, and each word she said is very real and easy to understand.
    Thanks Dr. Angela
    Abdallah Galal

  3. Jiaqing Fan

    Very good course. Dr Angela explains things very clearly. The exercises are interactive and helpful.

    Angela’s voice is like ASMR in most lessons. So you won’t lose focus?

  4. Jaime Sánchez Blanco

    Every concept is very well explained so you can assimilate it, and the teacher insists in practicing daily to get the habit of programming. It really motivates to keep studying and practicing every day.

  5. Bradley Carouthers

    3 days in and I’ve already learned more than other training programs where I’ve put months in. Her teaching style is both efficient and effective.

    Edit: Now on day 29 and the course has held up my expectations and hopes. The rise in complexity is not too sharp, and every day feels like an accomplishment. Angela takes her time to explain things without rushing, yet is able to pack tons of information in nuggets of time.

    Still recommend supplemental exposure for a more wholesome grasp. Though, this should be quite enough

  6. Tommy Prévost

    Very good course.

    PROs: lots of coding exercises, clear explanations, great examples, adapted to 2021’s reality.

    CONs: past the first 30 lessons, many similar coding hands-on.

  7. Quang Tu

    I love all the lectures and challenges of this course. I finally find myself in love with coding. This is definitely a fun course. Thank you Angela for this great course.

  8. Fortunate Eze

    This course is nothing short of AMAZING. The level of love and sincere desire to see students succeed present in this course is a rare sight. This course is fun, it’s challenging, it’s suspense-filled, it makes you feel good about yourself solving those challenges. You don’t take this course and memorise codes, you take it and you know what you’re doing because you literally do it yourself.

    It couldn’t possibly be any more obvious that Angela brought out her whole heart, time, energy and resources to give us a course that if priced based on it’s value, many of us certainly can’t afford. I mean, she kept advising and encouraging us to keep going, because she knows the road could get bumpy, she made a few jokes and used funny animations so we smile along the way keeping it fun. Why some people can’t see this and would rather subscribe to trying to bend courses to fit their own personal preferences is beyond me, but I guess people will always be people.

    Dear Angela, I cannot speak for everyone but I certainly can speak for myself and I’m saying thank you. The efforts you put into making this course outstanding didn’t go unnoticed.

    Let as many of us as appreciate your efforts make you happy and energize you to create more life changing courses for us.

    Cheers.

  9. Chendaniel

    Just the course fits me. Learn by doing, it is easy to start and just follow the flow and at day 15 you realized how to program in Python. Then the magic happen. I am on day 27 and I am looking forward to start a new section everyday.

  10. Caspar Cheng

    Angela is my favourite teacher on Udemy, her courses always come with clear and concise demonstration, make it really easy to understand the content. Meanwhile, she also provide a lot of tips to encourage us moving further on the way of coding. I’m definitely a super fan of her. I had finished her web development boot camp and now I’m learning Python 100 days of code. This course is amazing as she provided one particular project for each day’s learning, which is, really useful and handy. Also, those projects are really interesting and won’t let me feel disappointed about myself. Although some of them are a little bit difficult, they’re still resolvable after some struggling and searching online. I’ll recommend this course for those who really want to learn Python well and write real python projects from scratch!

  11. Jonas Weber

    The course was awesome in the first half! Really helped me to get back to programming and learning new techniques. Also the course got way to much focused on web development in the second half, which I’m not really interested in and is redundand to your dedicated web development course. The lack of videos in the last quarter was rather sad. I really would’ve liked some video explanations for pandas and matplotlib. The challenges and capstone projects were great and challenging!
    All in all I can really recommend this course for beginners and those who want to learn python and/or get back to programming!

  12. Rohit Prasad

    Angela is the best teacher I have ever experienced. The way she explains all the topics and makes use of it in the projects is commendable. Thank you so much Angela for this course.

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