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.

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

Frequently bought together

Designing Machine Learning Systems with Python

Mex$1045.99

Learning Predictive Analytics with Python

Mex$1128.99

Total Mex$ 2,174.98

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Dimitri Shvorob Aug 18, 2016

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