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 eye and nose detectors

The Haar cascades method can be extended to detect all types of objects. Let's see how to use it to detect the eyes and nose in the input video.

How to do it…

  1. Create a new Python file, and import the following packages:

    import cv2
    import numpy as np
  2. Load the face, eyes, and nose cascade files:

    # Load face, eye, and nose cascade files
    face_cascade = cv2.CascadeClassifier('cascade_files/haarcascade_frontalface_alt.xml')
    eye_cascade = cv2.CascadeClassifier('cascade_files/haarcascade_eye.xml')
    nose_cascade = cv2.CascadeClassifier('cascade_files/haarcascade_mcs_nose.xml')
  3. Check whether the files loaded correctly:

    # Check if face cascade file has been loaded
    if face_cascade.empty():
        raise IOError('Unable to load the face cascade classifier xml file')
    # Check if eye cascade file has been loaded
    if eye_cascade.empty():
        raise IOError('Unable to load the eye cascade classifier xml file')
    # Check if nose cascade file has been loaded
    if nose_cascade.empty():