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 linear classifier using Support Vector Machine (SVMs)

SVMs are supervised learning models that are used to build classifiers and regressors. An SVM finds the best separating boundary between the two sets of points by solving a system of mathematical equations. If you are not familiar with SVMs, here are a couple of good tutorials to get started:




Let's see how to build a linear classifier using an SVM.

Getting ready

Let's visualize our data to understand the problem at hand. We will use that's already provided to you as a reference. Before we build the SVM, let's understand our data. We will use the data_multivar.txt file that's already provided to you. Let's see how to to visualize the data. Create a new Python file and add the following lines to it:

import numpy as np
import matplotlib.pyplot as plt

import utilities 

# Load input data