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Designing Machine Learning Systems with Python
Designing Machine Learning Systems with Python

Designing Machine Learning Systems with Python: Key design strategies to create intelligent systems

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Profile Icon David Julian
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Paperback Apr 2016 232 pages 1st Edition
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Arrow left icon
Profile Icon David Julian
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Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2 (1 Ratings)
Paperback Apr 2016 232 pages 1st Edition
eBook
Mex$561.99 Mex$803.99
Paperback
Mex$1004.99
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Mex$561.99 Mex$803.99
Paperback
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Designing Machine Learning Systems with Python

Chapter 2. Tools and Techniques

Python comes equipped with a large library of packages for machine learning tasks.

The packages we will look at in this chapter are as follows:

  • The IPython console
  • NumPy, which is an extension that adds support for multi-dimensional arrays, matrices, and high-level mathematical functions
  • SciPy, which is a library of scientific formulae, constants, and mathematical functions
  • Matplotlib, which is for creating plots
  • Scikit-learn, which is a library for machine learning tasks such as classification, regression, and clustering

There is only enough space to give you a flavor of these huge libraries, and an important skill is being able to find and understand the reference material for the various packages. It is impossible to present all the different functionality in a tutorial style documentation, and it is important to be able to find your way around the sometimes dense API references. A thing to remember is that the majority of these packages are put together...

Python for machine learning

Python is a versatile general purpose programming language. It is an interpreted language and can run interactively from a console. It does not require a compiler like C++ or Java, so the development time tends to be shorter. It is available for free download and can be installed on many different operating systems including UNIX, Windows, and Macintosh. It is especially popular for scientific and mathematical applications. Python is relatively easy to learn compared to languages such as C++ and Java, with similar tasks using fewer lines of code.

Python is not the only platform for machine learning, but it is certainly one of the most used. One of its major alternatives is R. Like Python, it is open source, and while it is popular for applied machine learning, it lacks the large development community of Python. R is a specialized tool for machine learning and statistical analysis. Python is a general-purpose, widely-used programming language that also has excellent...

IPython console

The Ipython package has had some significant changes with the release of version 4. A former monolithic package structure, it has been split into sub-packages. Several IPython projects have split into their own separate project. Most of the repositories have been moved to the Jupyter project (jupyter.org).

At the core of IPython is the IPython console: a powerful interactive interpreter that allows you to test your ideas in a very fast and intuitive way. Instead of having to create, save, and run a file every time you want to test a code snippet, you can simply type it into a console. A powerful feature of IPython is that it decouples the traditional read-evaluate-print loop that most computing platforms are based on. IPython puts the evaluate phase into its own process: a kernel (not to be confused with the kernel function used in machine learning algorithms). Importantly, more than one client can access the kernel. This means you can run code in a number of files and access...

Installing the SciPy stack

The SciPy stack consists of Python along with the most commonly used scientific, mathematical, and ML libraries. (visit: scipy.org). These include NumPy, Matplotlib, the SciPy library itself, and IPython. The packages can be installed individually on top of an existing Python installation, or as a complete distribution (distro). The easiest way to get started is using a distro, if you have not got Python installed on your computer. The major Python distributions are available for most platforms, and they contain everything you need in one package. Installing all the packages and their dependencies separately does take some time, but it may be an option if you already have a configured Python installation on your machine.

Most distributions give you all the tools you need, and many come with powerful developer environments. Two of the best are Anaconda (www.continuum.io/downloads) and Canopy (http://www.enthought.com/products/canopy/). Both have free and commercial...

NumPY

We should know that there is a hierarchy of types for representing data in Python. At the root are immutable objects such as integers, floats, and Boolean. Built on this, we have sequence types. These are ordered sets of objects indexed by non-negative integers. They are iterative objects that include strings, lists, and tuples. Sequence types have a common set of operations such as returning an element (s[i]) or a slice (s[i:j]), and finding the length (len(s)) or the sum (sum(s)). Finally, we have mapping types. These are collections of objects indexed by another collection of key objects. Mapping objects are unordered and are indexed by numbers, strings, or other objects. The built-in Python mapping type is the dictionary.

NumPy builds on these data objects by providing two further objects: an N-dimensional array object (ndarray) and a universal function object (ufunc). The ufunc object provides element-by-element operations on ndarray objects, allowing typecasting and array broadcasting...

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...

Python for machine learning


Python is a versatile general purpose programming language. It is an interpreted language and can run interactively from a console. It does not require a compiler like C++ or Java, so the development time tends to be shorter. It is available for free download and can be installed on many different operating systems including UNIX, Windows, and Macintosh. It is especially popular for scientific and mathematical applications. Python is relatively easy to learn compared to languages such as C++ and Java, with similar tasks using fewer lines of code.

Python is not the only platform for machine learning, but it is certainly one of the most used. One of its major alternatives is R. Like Python, it is open source, and while it is popular for applied machine learning, it lacks the large development community of Python. R is a specialized tool for machine learning and statistical analysis. Python is a general-purpose, widely-used programming language that also has excellent...

IPython console


The Ipython package has had some significant changes with the release of version 4. A former monolithic package structure, it has been split into sub-packages. Several IPython projects have split into their own separate project. Most of the repositories have been moved to the Jupyter project (jupyter.org).

At the core of IPython is the IPython console: a powerful interactive interpreter that allows you to test your ideas in a very fast and intuitive way. Instead of having to create, save, and run a file every time you want to test a code snippet, you can simply type it into a console. A powerful feature of IPython is that it decouples the traditional read-evaluate-print loop that most computing platforms are based on. IPython puts the evaluate phase into its own process: a kernel (not to be confused with the kernel function used in machine learning algorithms). Importantly, more than one client can access the kernel. This means you can run code in a number of files and access...

Installing the SciPy stack


The SciPy stack consists of Python along with the most commonly used scientific, mathematical, and ML libraries. (visit: scipy.org). These include NumPy, Matplotlib, the SciPy library itself, and IPython. The packages can be installed individually on top of an existing Python installation, or as a complete distribution (distro). The easiest way to get started is using a distro, if you have not got Python installed on your computer. The major Python distributions are available for most platforms, and they contain everything you need in one package. Installing all the packages and their dependencies separately does take some time, but it may be an option if you already have a configured Python installation on your machine.

Most distributions give you all the tools you need, and many come with powerful developer environments. Two of the best are Anaconda (www.continuum.io/downloads) and Canopy (http://www.enthought.com/products/canopy/). Both have free and commercial...

NumPY


We should know that there is a hierarchy of types for representing data in Python. At the root are immutable objects such as integers, floats, and Boolean. Built on this, we have sequence types. These are ordered sets of objects indexed by non-negative integers. They are iterative objects that include strings, lists, and tuples. Sequence types have a common set of operations such as returning an element (s[i]) or a slice (s[i:j]), and finding the length (len(s)) or the sum (sum(s)). Finally, we have mapping types. These are collections of objects indexed by another collection of key objects. Mapping objects are unordered and are indexed by numbers, strings, or other objects. The built-in Python mapping type is the dictionary.

NumPy builds on these data objects by providing two further objects: an N-dimensional array object (ndarray) and a universal function object (ufunc). The ufunc object provides element-by-element operations on ndarray objects, allowing typecasting and array broadcasting...

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Key benefits

  • Gain an understanding of the machine learning design process
  • Optimize machine learning systems for improved accuracy
  • Understand common programming tools and techniques for machine learning
  • Develop techniques and strategies for dealing with large amounts of data from a variety of sources
  • Build models to solve unique tasks

Description

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.

Who is this book for?

This book is for data scientists, scientists, or just the curious. To get the most out of this book, you will need to know some linear algebra and some Python, and have a basic knowledge of machine learning concepts.

What you will learn

  • Gain an understanding of the machine learning design process
  • Optimize the error function of your machine learning system
  • Understand the common programming patterns used in machine learning
  • Discover optimizing techniques that will help you get the most from your data
  • Find out how to design models uniquely suited to your task

Product Details

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Publication date : Apr 06, 2016
Length: 232 pages
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Language : English
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Length: 232 pages
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ISBN-13 : 9781785882951
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Table of Contents

10 Chapters
1. Thinking in Machine Learning Chevron down icon Chevron up icon
2. Tools and Techniques Chevron down icon Chevron up icon
3. Turning Data into Information Chevron down icon Chevron up icon
4. Models – Learning from Information Chevron down icon Chevron up icon
5. Linear Models Chevron down icon Chevron up icon
6. Neural Networks Chevron down icon Chevron up icon
7. Features – How Algorithms See the World Chevron down icon Chevron up icon
8. Learning with Ensembles Chevron down icon Chevron up icon
9. Design Strategies and Case Studies Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

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Dimitri Shvorob Aug 18, 2016
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Wishing to learn Python's machine-learning toolkit - I am an emigrant from R Country - I rounded up several relevant books, and set out to narrow the field to one or two suitable for further study. My haul included (in no particular order)"Machine Learning in Python" by Bowles, published in 2015 by Wiley, 360 pages, $25 for the cheapest hard-copy now available from Amazon (including shipping)"Designing Machine Learning Systems with Python" by Julian, 2016, Packt, 232 pages, $42"Mastering Python for Data Science" by Madhavan, 2015, Packt, 294 pages, $39"Learning Data Mining with Python" by Layton, 2015, 369 pages, $43"Python Data Science Cookbook" by Subramanian, 2015, 347 pages, $48"Data Science From Scratch" by Grus, 2015, 330 pages, $24"Learning scikit-learn" by Moncecchi and Garreta, 2013, 118 pages, $28"Building Machine Learning Systems with Python" by Coelho and Richert, 2015, 305 pages, $49"Python Machine Learning" by Raschka, 2015, 454 pages, $34Seven titles into the task (Grus, Julian, Bowles, Moncecchi and Garreta, Madhavan, Layton, Julian), I realized that this sequential elimination would not be a walk in the park: while there are plenty of ignorable stinkers - invariably produced by Packt - among R-based data-science books, Python-flavor offerings tend to be pretty decent. I was able to discard Madhavan's inferior book - then, the thin Moncecchi-Garreta was an easy pass as hardcopy, but the cheap Kindle edition was somebody's "maybe". On to Layton - a strong book - and then back to Julian, which, I think, is going to be the next departure. It is not a bad book, and definitely not a copycat as so many bad books are. In fact, it may be too original for its own good, as the author's musings and tangents leave fewer pages for the less original but more bread-and-butter stuff, in a book which does not have that many pages to begin with. From this bread-and-butter perspective, the book lags behind even Moncecchi and Garreta. So pass, and on to Subramanian...
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