Building Machine Learning Systems with Python (Anglais) Broché – 26 juillet 2013
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Description du produit
Présentation de l'éditeur
As the Big Data explosion continues at an almost incomprehensible rate, being able to understand and process it becomes even more challenging. With Building Machine Learning Systems with Python, you'll learn everything you need to tackle the modern data deluge - by harnessing the unique capabilities of Python and its extensive range of numerical and scientific libraries, you will be able to create complex algorithms that can 'learn' from data, allowing you to uncover patterns, make predictions, and gain a more in-depth understanding of your data.
Featuring a wealth of real-world examples, this book provides gives you with an accessible route into Python machine learning. Learn the Iris dataset, find out how to build complex classifiers, and get to grips with clustering through practical examples that deliver complex ideas with clarity. Dig deeper into machine learning, and discover guidance on classification and regression, with practical machine learning projects outlining effective strategies for sentiment analysis and basket analysis. The book also takes you through the latest in computer vision, demonstrating how image processing can be used for pattern recognition, as well as showing you how to get a clearer picture of your data and trends by using dimensionality reduction.
Keep up to speed with one of the most exciting trends to emerge from the world of data science and dig deeper into your data with Python with this unique data science tutorial.
- Learn how to create machine learning algorithms using the flexibility of Python
- Get to grips with scikit-learn and other Python scientific libraries that support machine learning projects
- Employ computer vision using mahotas for image processing that will help you uncover patterns and trends in your data
- Learn topic modelling and build a topic model for Wikipedia
- Analyze Twitter data using sentiment analysis
- Get to grips with classification and regression with real-world examples
Biographie de l'auteur
Willi Richert has a PhD in Machine Learning/Robotics and currently works for Microsoft in the Bing Core Relevance Team. He performs statistical machine translation.
Luis Pedro Coelho
Luis Pedro Coelho is a Computational Biologist: someone who uses computers as a tool to understand biological systems. Within this large field, Luis works in Bioimage Informatics, which is the application of machine learning techniques to the analysis of images of biological specimens. His main focus is on the processing of large scale image data. With robotic microscopes, it is possible to acquire hundreds of thousands of images in a day, and visual inspection of all the images becomes impossible. Luis has a PhD from Carnegie Mellon University, which is one of the leading universities in the world in the area of machine learning. He is also the author of several scientific publications. Luis started developing open source software in 1998 as a way to apply to real code what he was learning in his computer science courses at the Technical University of Lisbon. In 2004, he started developing in Python and has contributed to several open source libraries in this language. He is the lead developer on mahotas, the popular computer vision package for Python, and is the contributor of several machine learning codes..
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Commentaires client les plus utiles sur Amazon.com
I offer this Thumbs-Up review with years writing experience: When, in the process of transferring an environment that I've designed and built to ops personnel, I many times had to write thick documents that encompassed many layers: describing the theory; conjuring up examples and analogies to cement concepts; inserting diagrams at key locations to bring points home; context switching between the overall subject and providing quick tutorials on underlying tools used; spending time showing how not to do something, and then showing how to do it; and of course writing good English -- i.e. in an understandable way, which requires a lot of re-edits. A small example of what I mean (something that took me a solid week to write) is here: didata.us/2014/06/11/logistic-regression-machine-learning
That said, the authors of this book did a splendid job weaving through all of those layers, in what I'm sure was a tight time frame: theory versus practical; right versus wrong way; a little numpy, a little statistical concepts; dreaming up examples, which are not trivial; and so on. They couldn't cover everything, of course... No book today has the completeness and calibre of a Richard W. Stallings TCP/IP Volume I, II, and III book. But you can write a book that, while not a bible, provides a lot of information and pointers that you hope the initiated reader will run with. The authors here did that well.
You can forgive my 1-star deduction. That falls on Packt Publishing.
The book illustrates many useful machine learning techniques with well thought out explanations.
Willi Richert, has been quite helpful and has looked at the issues I was having and resolved some of them, so especially if you are working on Windows, make sure you get the code from GitHub.
I have not returned to complete working through the rest book as yet, will as soon as I have time.
To be completely honest I had great hope for this book, it was theoretically exactly what I was looking for, a practical guide to getting up and running with Machine Learning and some of it major Python packages.
From chapter 3, there were code discrepancies between what was in the book, what was supplied and then eventually what I got working...
I am not going to bother going into all the errors / issues, the 2 major ones that made me "shelve" the book and start looking for new study material:
1. After the 9GB download for chapter 5, the supplied source doesn't work and contains requirements to 32bit libs... huge waste of time...
2. After moving onto in chapter 6, and after 24 hours of downloading tweets for sentiment analysis... I checked the files and they only contained "The Twitter REST API v1 is no longer active. Please migrate to API v1.1".
Yes, I could go debug and fix the code / errors in other peoples code... but that is not how I want to spend my time learning a new subject, I have enough of that in my day job as a software developer :)
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