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 a face detector using Haar cascades

As we discussed earlier, face detection is the process of determining the location of the face in the input image. We will use Haar cascades for face detection. This works by extracting a large number of simple features from the image at multiple scales. The simple features are basically edge, line, and rectangle features that are very easy to compute. It is then trained by creating a cascade of simple classifiers. The Adaptive Boosting technique is used to make this process robust. You can learn more about it at Let's take a look at how to determine the location of a face in the video frames captured from the webcam.

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 detector cascade file. This is a trained model that we can use as a detector:

    # Load the face cascade file
    face_cascade = cv2.CascadeClassifier...