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Designing Machine Learning Systems with Python

Designing Machine Learning Systems with Python

By : David Julian
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Designing Machine Learning Systems with Python

Designing Machine Learning Systems with Python

2 (1)
By: David Julian

Overview of this book

Machine learning is one of the fastest growing trends in modern computing. It has applications in a wide range of fields, including economics, the natural sciences, web development, and business modeling. In order to harness the power of these systems, it is essential that the practitioner develops a solid understanding of the underlying design principles. There are many reasons why machine learning models may not give accurate results. By looking at these systems from a design perspective, we gain a deeper understanding of the underlying algorithms and the optimisational methods that are available. This book will give you a solid foundation in the machine learning design process, and enable you to build customised machine learning models to solve unique problems. You may already know about, or have worked with, some of the off-the-shelf machine learning models for solving common problems such as spam detection or movie classification, but to begin solving more complex problems, it is important to adapt these models to your own specific needs. This book will give you this understanding and more.
Table of Contents (11 chapters)
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1
1. Thinking in Machine Learning
10
Index

Matplotlib

Matplotlib, or more importantly, its sub-package PyPlot, is an essential tool for visualizing two-dimensional data in Python. I will only mention it briefly here because its use should become apparent as we work through the examples. It is built to work like Matlab with command style functions. Each PyPlot function makes some change to a PyPlot instance. At the core of PyPlot is the plot method. The simplest implementation is to pass plot a list or a 1D array. If only one argument is passed to plot, it assumes it is a sequence of y values, and it will automatically generate the x values. More commonly, we pass plot two 1D arrays or lists for the co-ordinates x and y. The plot method can also accept an argument to indicate line properties such as line width, color, and style. Here is an example:

import numpy as np
import matplotlib.pyplot as plt

x = np.arange(0., 5., 0.2)
plt.plot(x, x**4, 'r', x, x*90, 'bs', x, x**3, 'g^')
plt.show()

This code prints...

CONTINUE READING
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