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

Performing blind source separation

Blind source separation refers to the process of separating signals from a mixture. Let's say a bunch of different signal generators generate signals and a common receiver receives all of these signals. Now, our job is to separate these signals from this mixture using the properties of these signals. We will use Independent Components Analysis (ICA) to achieve this. You can learn more about it at Let's see how to do it.

How to do it…

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

    import numpy as np
    import matplotlib.pyplot as plt
    from scipy import signal
    from sklearn.decomposition import PCA, FastICA 
  2. We will use data from the mixture_of_signals.txt file that's already provided to you. Let's load the data:

    # Load data
    input_file = 'mixture_of_signals.txt'
    X = np.loadtxt(input_file)
  3. Create the ICA object:

    # Compute ICA
    ica = FastICA(n_components=4)
  4. Reconstruct the signals, based...