Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Building Machine Learning Systems with Python
Building Machine Learning Systems with Python

Building Machine Learning Systems with Python: Expand your Python knowledge and learn all about machine-learning libraries in this user-friendly manual. ML is the next big breakthrough in technology and this book will give you the head-start you need.

eBook
€22.99 €32.99
Paperback
€41.99
Subscription
Free Trial
Renews at €18.99p/m

What do you get with Print?

Product feature icon Instant access to your digital eBook copy whilst your Print order is Shipped
Product feature icon Paperback book shipped to your preferred address
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
OR
Modal Close icon
Payment Processing...
tick Completed

Shipping Address

Billing Address

Shipping Methods
Table of content icon View table of contents Preview book icon Preview Book

Building Machine Learning Systems with Python

Chapter 2. Learning How to Classify with Real-world Examples

Can a machine distinguish between flower species based on images? From a machine learning perspective, we approach this problem by having the machine learn how to perform this task based on examples of each species so that it can classify images where the species are not marked. This process is called classification (or supervised learning), and is a classic problem that goes back a few decades.

We will explore small datasets using a few simple algorithms that we can implement manually. The goal is to be able to understand the basic principles of classification. This will be a solid foundation to understanding later chapters as we introduce more complex methods that will, by necessity, rely on code written by others.

The Iris dataset


The Iris dataset is a classic dataset from the 1930s; it is one of the first modern examples of statistical classification.

The setting is that of Iris flowers, of which there are multiple species that can be identified by their morphology. Today, the species would be defined by their genomic signatures, but in the 1930s, DNA had not even been identified as the carrier of genetic information.

The following four attributes of each plant were measured:

  • Sepal length

  • Sepal width

  • Petal length

  • Petal width

In general, we will call any measurement from our data as features.

Additionally, for each plant, the species was recorded. The question now is: if we saw a new flower out in the field, could we make a good prediction about its species from its measurements?

This is the supervised learning or classification problem; given labeled examples, we can design a rule that will eventually be applied to other examples. This is the same setting that is used for spam classification; given the...

Building more complex classifiers


In the previous section, we used a very simple model: a threshold on one of the dimensions. Throughout this book, you will see many other types of models, and we're not even going to cover everything that is out there.

What makes up a classification model? We can break it up into three parts:

  • The structure of the model: In this, we use a threshold on a single feature.

  • The search procedure: In this, we try every possible combination of feature and threshold.

  • The loss function: Using the loss function, we decide which of the possibilities is less bad (because we can rarely talk about the perfect solution). We can use the training error or just define this point the other way around and say that we want the best accuracy. Traditionally, people want the loss function to be minimum.

We can play around with these parts to get different results. For example, we can attempt to build a threshold that achieves minimal training error, but we will only test three values...

A more complex dataset and a more complex classifier


We will now look at a slightly more complex dataset. This will motivate the introduction of a new classification algorithm and a few other ideas.

Learning about the Seeds dataset

We will now look at another agricultural dataset; it is still small, but now too big to comfortably plot exhaustively as we did with Iris. This is a dataset of the measurements of wheat seeds. Seven features are present, as follows:

  • Area (A)

  • Perimeter (P)

  • Compactness ()
  • Length of kernel

  • Width of kernel

  • Asymmetry coefficient

  • Length of kernel groove

There are three classes that correspond to three wheat varieties: Canadian, Koma, and Rosa. As before, the goal is to be able to classify the species based on these morphological measurements.

Unlike the Iris dataset, which was collected in the 1930s, this is a very recent dataset, and its features were automatically computed from digital images.

This is how image pattern recognition can be implemented: you can take images in...

Binary and multiclass classification


The first classifier we saw, the threshold classifier, was a simple binary classifier (the result is either one class or the other as a point is either above the threshold or it is not). The second classifier we used, the nearest neighbor classifier, was a naturally multiclass classifier (the output can be one of several classes).

It is often simpler to define a simple binary method than one that works on multiclass problems. However, we can reduce the multiclass problem to a series of binary decisions. This is what we did earlier in the Iris dataset in a haphazard way; we observed that it was easy to separate one of the initial classes and focused on the other two, reducing the problem to two binary decisions:

  • Is it an Iris Setosa (yes or no)?

  • If no, check whether it is an Iris Virginica (yes or no).

Of course, we want to leave this sort of reasoning to the computer. As usual, there are several solutions to this multiclass reduction.

The simplest is to use...

Summary


In a sense, this was a very theoretical chapter, as we introduced generic concepts with simple examples. We went over a few operations with a classic dataset. This, by now, is considered a very small problem. However, it has the advantage that we were able to plot it out and see what we were doing in detail. This is something that will be lost when we move on to problems with many dimensions and many thousands of examples. The intuitions we gained here will all still be valid.

Classification means generalizing from examples to build a model (that is, a rule that can automatically be applied to new, unclassified objects). It is one of the fundamental tools in machine learning, and we will see many more examples of this in forthcoming chapters.

We also learned that the training error is a misleading, over-optimistic estimate of how well the model does. We must, instead, evaluate it on testing data that was not used for training. In order to not waste too many examples in testing, a cross...

Left arrow icon Right arrow icon

Key benefits

  • Master Machine Learning using a broad set of Python libraries and start building your own Python-based ML systems
  • Covers classification, regression, feature engineering, and much more guided by practical examples
  • A scenario-based tutorial to get into the right mind-set of a machine learner (data exploration) and successfully implement this in your new or existing projects

Description

Machine learning, the field of building systems that learn from data, is exploding on the Web and elsewhere. Python is a wonderful language in which to develop machine learning applications. As a dynamic language, it allows for fast exploration and experimentation and an increasing number of machine learning libraries are developed for Python.Building Machine Learning system with Python shows you exactly how to find patterns through raw data. The book starts by brushing up on your Python ML knowledge and introducing libraries, and then moves on to more serious projects on datasets, Modelling, Recommendations, improving recommendations through examples and sailing through sound and image processing in detail. Using open-source tools and libraries, readers will learn how to apply methods to text, images, and sounds. You will also learn how to evaluate, compare, and choose machine learning techniques. Written for Python programmers, Building Machine Learning Systems with Python teaches you how to use open-source libraries to solve real problems with machine learning. The book is based on real-world examples that the user can build on. Readers will learn how to write programs that classify the quality of StackOverflow answers or whether a music file is Jazz or Metal. They will learn regression, which is demonstrated on how to recommend movies to users. Advanced topics such as topic modeling (finding a text's most important topics), basket analysis, and cloud computing are covered as well as many other interesting aspects.Building Machine Learning Systems with Python will give you the tools and understanding required to build your own systems, which are tailored to solve your problems.

Who is this book for?

This book is for Python programmers who are beginners in machine learning, but want to learn Machine learning. Readers are expected to know Python and be able to install and use open-source libraries. They are not expected to know machine learning, although the book can also serve as an introduction to some Python libraries for readers who know machine learning. This book does not go into the detail of the mathematics behind the algorithms.This book primarily targets Python developers who want to learn and build machine learning in their projects, or who want to provide machine learning support to their existing projects, and see them getting implemented effectively.

What you will learn

  • Build a classification system that can be applied to text, images, or sounds
  • Use scikit-learn, a Python open-source library for machine learning
  • Explore the mahotas library for image processing and computer vision
  • Build a topic model of the whole of Wikipedia
  • Get to grips with recommendations using the basket analysis
  • Use the Jug package for data analysis
  • Employ Amazon Web Services to run analyses on the cloud
  • Recommend products to users based on past purchases
Estimated delivery fee Deliver to Greece

Premium delivery 7 - 10 business days

€17.95
(Includes tracking information)

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : Jul 26, 2013
Length: 290 pages
Edition : 1st
Language : English
ISBN-13 : 9781782161400
Category :
Languages :

What do you get with Print?

Product feature icon Instant access to your digital eBook copy whilst your Print order is Shipped
Product feature icon Paperback book shipped to your preferred address
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
OR
Modal Close icon
Payment Processing...
tick Completed

Shipping Address

Billing Address

Shipping Methods
Estimated delivery fee Deliver to Greece

Premium delivery 7 - 10 business days

€17.95
(Includes tracking information)

Product Details

Publication date : Jul 26, 2013
Length: 290 pages
Edition : 1st
Language : English
ISBN-13 : 9781782161400
Category :
Languages :

Packt Subscriptions

See our plans and pricing
Modal Close icon
€18.99 billed monthly
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Simple pricing, no contract
€189.99 billed annually
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just €5 each
Feature tick icon Exclusive print discounts
€264.99 billed in 18 months
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just €5 each
Feature tick icon Exclusive print discounts

Frequently bought together


Stars icon
Total 129.97
Practical Data Analysis
€41.99
Machine Learning with R
€45.99
Building Machine Learning Systems with Python
€41.99
Total 129.97 Stars icon
Banner background image

Table of Contents

12 Chapters
Getting Started with Python Machine Learning Chevron down icon Chevron up icon
Learning How to Classify with Real-world Examples Chevron down icon Chevron up icon
Clustering – Finding Related Posts Chevron down icon Chevron up icon
Topic Modeling Chevron down icon Chevron up icon
Classification – Detecting Poor Answers Chevron down icon Chevron up icon
Classification II – Sentiment Analysis Chevron down icon Chevron up icon
Regression – Recommendations Chevron down icon Chevron up icon
Regression – Recommendations Improved Chevron down icon Chevron up icon
Classification III – Music Genre Classification Chevron down icon Chevron up icon
Computer Vision – Pattern Recognition Chevron down icon Chevron up icon
Dimensionality Reduction Chevron down icon Chevron up icon
Big(ger) Data Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.9
(38 Ratings)
5 star 42.1%
4 star 28.9%
3 star 10.5%
2 star 15.8%
1 star 2.6%
Filter icon Filter
Top Reviews

Filter reviews by




Marc-Anthony Taylor Sep 30, 2013
Full star icon Full star icon Full star icon Full star icon Full star icon 5
(Full disclosure I received a review copy of this book for free.)I have been interested in machine learning for quite a while now but most of the texts I have come across have been fairly dry and often either connected to a language in which I can't programme or not connected to one at all taking a rather abstract approach."Building Machine Learning Systems with Python" is an excellent exception. For a beginner this book is perfect. The only assumption the author's make is that you know your way around Python and that it is installed on your machine (which is a fairly safe bet if you pick up the book).The book itself is split into 12 chapters.The first chapter eases you in to the subject with an introduction to some of the tools and libraries (numpy, scipy, etc) you will use and even a first, albeit small, machine learning application.Chapter 2 deals with classification, describing how to visualise the data and build a model.Chapter 3 introduces clustering and offers examples both on how and how not to group data particularly text. It also includes a brief introduction to the Native Language Toolkit (NLTK).Topic modelling is discussed in the fourth chapter. Here we are also introduced to new terms and tools.Chapter 5 takes us back to classification. Here we learn to differentiate between `good' and `bad' answers and such topics as bias and variance.In chapter 6 we look at sentiment analysis, using twitter as an example, and we get to know a little about Bayes Theorem.Chapters 7 & 8 talk about regression and recommendation; finding the best ways to offer users relevant information.Classification rears its head again in chapter 9. This was one of the most difficult chapters for me to follow although extremely interesting. The example is music genre classification and can be quite mathematically intense.Chapter 10 is all about computer vision and pattern recognition, we touch on image processing and dealing with noise.Chapter 11 helps to break down the data making it easier to process.And chapter 12 brings it all together discussing big data, multi-core processing and even an intro to AWS. We are encouraged to use that which we have learned with interesting examples.All in all I found this a terrific introduction. The book's conversational tone and interesting teaching style make for a great read. I do feel that I have a good base for learning more. I can't recommend this book enough.
Amazon Verified review Amazon
Alex Dec 09, 2013
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A very accessible book with awesome examples. You don't need to have a very deep understanding of machine learning to find this book useful. Recommended for beginners with not much sophisticated mathematical knowledge.
Amazon Verified review Amazon
Sujit Pal Oct 21, 2013
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Python has an excellent ecosystem of libraries for Machine Learning. The libraries are all well-documented but sometimes it is hard to figure out how to solve a problem end-to-end using one or more of these libraries. This book attempts to fill that niche. It contains 12 chapters, each focusing on one or two ML problems, and shows how an expert ML practitioner would build and evaluate solutions for these problems. The main focus of the book is on the famous Scikits-Learn library, along with its dependencies Numpy and Scipy, but there is also coverage of gensim (for topic modeling), mahotas (for image processing), jug and starcluster (for distributed computing). The tone of the book is very practical and hands-on, in the rare cases where theory is explained, it is done without math. At the same time, the book is much more than just an introduction to Python ML libraries - you will come away learning "insider secrets" that you can do to improve your solution and which are already available as API calls within one of these libraries.The authors say that this book was written to their younger selves - in my opinion a very accurate representation of the target audience. I believe the people who would benefit most from the book are those who are programming using the Python/sklearn ecosystem already at competitions or at work but who are still not at the top of their game. This book can help you get (at least part of the way) there.
Amazon Verified review Amazon
KJP Sep 29, 2013
Full star icon Full star icon Full star icon Full star icon Full star icon 5
It has been a great pleasure to read this book "Building Maching Learning Systems with Python".This book presents application of algorithms to real world problems from machine learning perspective. It demonstrates practical examples of solving machine learning issues with Python scripts. Analysis and reasoning follows the examples. It guides the readers with simple algorithms and extends to the more complex machine learning issues. Whether you are a software professional or non technical person, this book will serve as an introductory material to the world of machine learning. Whether you are involved in machine learning projects or not, this book will expand your horizon and may help you develop/design software solutions. I am a Python software engineer and particularly like the the machine learning tackled with Python. I therefore highly recommd this book to all who are in the IT field.
Amazon Verified review Amazon
Amazon Customer Jan 05, 2014
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I love this book. It provides a lot of practical, clear examples and explanations that a lot of other machine learning courses just don't provide or are too slow to reach (for my ADD). I love the way the author gives enough explanation for you to grasp the concepts involved with practical machine learning systems, without going into so much detail that you just give up halfway through. 5 stars, I'm definitely looking forward to more books of this quality from Willi Richert.Daniel
Amazon Verified review Amazon
Get free access to Packt library with over 7500+ books and video courses for 7 days!
Start Free Trial

FAQs

What is the delivery time and cost of print book? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela
What is custom duty/charge? Chevron down icon Chevron up icon

Customs duty are charges levied on goods when they cross international borders. It is a tax that is imposed on imported goods. These duties are charged by special authorities and bodies created by local governments and are meant to protect local industries, economies, and businesses.

Do I have to pay customs charges for the print book order? Chevron down icon Chevron up icon

The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges? Chevron down icon Chevron up icon

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

For example:

  • If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
  • Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.
How can I cancel my order? Chevron down icon Chevron up icon

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact [email protected] with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at [email protected] using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on [email protected] with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on [email protected] within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on [email protected] who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on [email protected] within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela