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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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Marc-Anthony Taylor Sep 30, 2013

(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.

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Alex Dec 09, 2013

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.

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Sujit Pal Oct 21, 2013

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.

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KJP Sep 29, 2013

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.

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Amazon Customer Jan 05, 2014

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

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.

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