These algorithms deal with common scenarios faced by all data analysts and data scientists. This book focuses on two algorithm families linear methods and ensemble methods that effectively predict outcomes. This type of problem covers a multitude of use cases what ad to place on a web page, predicting prices in securities markets, detecting credit card fraud, etc. The focus on two families gives enough room for full descriptions of the mechanisms at work in the algorithms.
Then the code examples serve to illustrate the workings of the machinery with specific hackable code. The author will explain in simple terms, using no complex math, how these algorithms work, and will then show how to apply them in Python. He will also provide advice on how to select from among these algorithms, and will show how to prepare the data, and how to use the trained models in practice. The author begins with an overview of the two core algorithms, explaining the types of problems solved by each one.
He then introduces a core set of Python programming techniques that can be used to apply these algorithms. The author shows various techniques for building predictive models that solve a range of problems, from simple to complex; he also shows how to measure the performance of each model to ensure you use the right one.
The following chapters provide a deep dive into each of the two algorithms: penalized linear regression and ensemble methods. Chapters will show how to apply each algorithm in Python. Readers can directly use the sample code to build their own solutions.
For readers who want to design Web pages that load quickly, are easy to update, accessible to all, work on all browsers and can be quickly adapted to different media, this comprehensive guide represents the best way to go about it.
With Web standards best practices at its heart, it outlines how to do things the right way from the outset, resulting in highly optimized web pages, in a quicker, easier, less painful way than users could hope for! Readers can also see the lessons in action with more than 70 online examples constructed especially for the book. Python is an easy to learn, powerful programming language.
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The same site also contains distributions of and pointers to many free third party Python modules, programs and tools, and additional documentation.
Python is also suitable as an extension language for customizable applications. This tutorial introduces the reader informally to the basic concepts and features of the python language and system. It helps to have a Python interpreter handy for hands-on experience, but all examples are self contained, so the tutorial can be read off-line as well.
For a description of standard objects and modules, see library-index. There are also several books covering Python in depth. This tutorial does not attempt to be comprehensive and cover every single feature, or even every commonly used feature. After reading it, you will be able to read and write Python modules and programs, and you will be ready to learn more about the various Python library modules described in library-index.
The Glossary is also worth going through. The authors then explain the specification in detail, describing the semantics and data model, before moving to examples using XQuery to manipulate XML databases and document storage systems.
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