Book Image

Python Machine Learning Cookbook

By : Prateek Joshi, Vahid Mirjalili
Book Image

Python Machine Learning Cookbook

By: Prateek Joshi, Vahid Mirjalili

Overview of this book

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you’ll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.
Table of Contents (19 chapters)
Python Machine Learning Cookbook
About the Author
About the Reviewer

Building Hidden Markov Models

We are now ready to discuss speech recognition. We will use Hidden Markov Models (HMMs) to perform speech recognition. HMMs are great at modeling time series data. As an audio signal is a time series signal, HMMs perfectly suit our needs. An HMM is a model that represents probability distributions over sequences of observations. We assume that the outputs are generated by hidden states. So, our goal is to find these hidden states so that we can model the signal. You can learn more about it at Before you proceed, you need to install the hmmlearn package. You can find the installation instructions at Let's take a look at how to build HMMs.

How to do it…

  1. Create a new Python file. Let's define a class to model HMMs:

    # Class to handle all HMM related processing
    class HMMTrainer(object):
  2. Let's initialize the class. We will use Gaussian HMMs to model our data. The n_components...