Unsupervised Machine Learning Hidden Markov Models in Python

Hidden Markov Model (HMM) is all about learning sequences.

HMM is a global branch of Machine Learning and a type of random process utilized to solve real-life complications as well as explain the possibilities of sequences of random variables. Since a lot of data is of greater importance for us to model in a sequence. Language is a sequence of words; stock prices are also kind of sequences of prices. Furthermore, credit scoring also involves sequences of borrowing and returning money – thus, we can utilize those sequences to analyze whether or not you are going to default. Sequences, in a nutshell, are everywhere, and could be able to assess and evaluate them is of utmost importance when it comes to your data science toolbox.

Imagine reading the same content you are reading right not in a different sequence – if I had written the upward sentence backwards, it would not have made sense to you, despite that it contained the same words. Hence, it is evident that order is essential and of paramount significance.

The fact that the current whim in deep learning is to utilize recurrent neural networks in order to model sequences, I would like to introduce you guys to a machine learning algorithm since it has been in the town for decades now – the Hidden Markov Model.

Course Insights!

The course “Hidden Markov Models in Pythons” follows from my initial course in Unsupervised Machine Learning for Cluster Analysis. In which you learn and understand how to effectively measure the probability distribution of a random variable. In addition, in this module, you will learn to measure the probability distribution of a sequence of random variables.

Well, you have a pretty idea how much I love Deep Learning, thus, there is an interesting twist in the module.

Also, we have already successfully covered gradient descent and you already have an idea about its significance in order to solve deep learning issues. I claimed that gradient descent can also be utilized to optimize any sort of objective function.

Additionally, in this module, you will learn and be able to utilize gradient descent in order to solve the optimal parameters of a “Hidden Markov Models”, as an alternative to the common expectation-maximization algorithm.

Theano, which is the most common library for deep learning will be the spot where we are going to do it. You will learn how to work with sequences in Theano. Also, it is very beneficial for covering LSTMs and recurrent neural networks.

To make it even more effective and interesting the course revolves around several practical applications pertaining to Hidden Markov Models and Markov Models. We would also look at a model of health and sickness, and learn, calculate and analyze how long you are going to remain sick, in case if you get sick.

In addition, we will discuss and talk about how one can use Markov models to assess how individuals interact and communicate with your websites as well as fix problem areas such as high bounce rate, which is very influential when it comes to Search Engines Optimization (SEO). We will also develop language models that could be utilized to recognize and assess a writer and even produce or generate content. Just imagine an artificial machine doing your writing for you.

In the course, further, we will discuss more practical applications in detail including smartphone autosuggestions, generating images, and utilizing HMMs to effectively answer a wide range of questions such as how is DNA is translated into the behavioral or physical attribute of an organism.

In addition, you can download or install this course for FREE. Also, lecturers are always available for you to answer any of your queries proficiently and help you along your machine learning journey.