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 an object recognizer

Now that we trained an ERF model, let's go ahead and build an object recognizer that can recognize the content of unknown images.

How to do it…

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

    import argparse 
    import cPickle as pickle 
    import cv2
    import numpy as np
    import build_features as bf
    from trainer import ERFTrainer 
  2. Define the argument parser:

    def build_arg_parser():
        parser = argparse.ArgumentParser(description='Extracts features \
    from each line and classifies the data')
        parser.add_argument("--input-image", dest="input_image", required=True,
    help="Input image to be classified")
        parser.add_argument("--model-file", dest="model_file", required=True,
    help="Input file containing the trained model")
        parser.add_argument("--codebook-file", dest="codebook_file", 
    required=True, help="Input file containing the codebook")
        return parser
  3. Define a class to handle the image tag extraction functions:

    class ImageTagExtractor(object):