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I was drawn to this book because I want to understand how to model data gushing from social networks and large data sets. Although I wasn't sure of how the book would approach the topics of data mining, I hypothesized that this book would give me some basic skills to leverage large data sets, at least giving me some examples of how data can be analyzed algorithmically.
I have discovered after reading the book that one can learn information pertaining to several skill sets:
1. Mining data from the internet and filtering that data. Since beginning to read the book, I have borrowed several techniques for gathering and saving data.
2. Introduction to about a dozen mathematical concepts and algorithms that use said data to filter, categorize, predict properties of, and determine relationships betweeen the data. I have realized that much entrepreneurial activity involved in enhancing human interaction is achieved through manipulation of data and these algorithms.
As far as the mathematics and algorithms themselves, much of it was more advanced than my current understanding, and will service in the long run as a springboard from which I must pursue these concepts. The book has given me a lot of starting points from further exploration.
3. An introduction to the python programming languages and some useful tools / packages written it it. Most of the python you will glean from this book is not laid out explicitly, but gathered from reading examples of data mining and analysis with python. Some basic syntax and script structure, as well as data types like lists and dictionaries, and functions for operating on data structures and performating mathematics are utilized.
Tools in python introduced in the book include FeedParser, Python Imaging Library, Beautiful Soup for html/xml parsing, pysqlite for database creation, NumPy for linear algebra and matrix mathematics, and matplotlib for 2D graphics.
Reading this book has piqued my interest in algorithms and mathematical analysis of datasets. From here I will pusue the study of these fields and a more in-depth understanding of some of the methods and algorithms presented. It was also a nice mental exercise to learn to read python code from real-world examples.

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This looks like the 21st century successor to the AI programming books of the previous century (mine, and Charniak, Riesbeck, & McDermott). Lots of interesting applications; you can learn from them without much background required.

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The emphasis of the book is on applications rather than theory, which is what you would expect from a machine learning book published by O'Reilly. The applications are interesting and implemented in python.

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