With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication.Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligenceThis book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

    It is a bridge from the computational courses (such as calculus or differential equations) that students typically encounter in their first year of college to a more abstract outlook. It lays a foundation for more theoretical courses such as topology, analysis and abstract algebra. Although it may be more meaningful to the student who has had some calculus, there is really no prerequisite other than a measure of mathematical maturity. Topics include sets, logic, counting, methods of conditional and non-conditional proof, disproof, induction, relations, functions and infinite cardinality.

    Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.With this handbook, you’ll learn how to use: * IPython and Jupyter: provide computational environments for data scientists using Python * NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python * Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python * Matplotlib: includes capabilities for a flexible range of data visualizations in Python * Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

    It is also a book they may want to keep out of view of their professors, for it expresses in clear and simple language things that tend to be murky and abstruse in the classroom. These 101 concise lessons in design, drawing, the creative process, and presentation--from the basics of "How to Draw a Line" to the complexities of color theory--provide a much-needed primer in architectural literacy, making concrete what too often is left nebulous or open-ended in the architecture curriculum. Each lesson utilizes a two-page format, with a brief explanation and an illustration that can range from diagrammatic to whimsical. The lesson on "How to Draw a Line" is illustrated by examples of good and bad lines; a lesson on the dangers of awkward floor level changes shows the television actor Dick Van Dyke in the midst of a pratfall; a discussion of the proportional differences between traditional and modern buildings features a drawing of a building split neatly in half between the two. Written by an architect and instructor who remembers well the fog of his own student days, 101 Things I Learned in Architecture School provides valuable guideposts for navigating the design studio and other classes in the architecture curriculum. Architecture graduates--from young designers to experienced practitioners--will turn to the book as well, for inspiration and a guide back to basics when solving a complex design problem.

    Increasingly, people are feeling powerless and underrepresented because they have no voice.Designers, however, have a voice. They are among the most influential bystanders because their skills enable them to communicate a message easily through the Web or through posters and printed pieces. A picture is worth a thousand words and designers have used this adage to their advantage for years by creating simple yet powerful designs that immediately convey the message to the viewer. The Design of Dissent focuses on graphic work that designers have made as a result of social and political concerns. The time is certainly ripe as the U.S., and world, flares in opposition on so many important issues.

    Several of the data display strategies from previous editions are now presented in re-envisioned and reorganized formats to enhance reader accessibility and comprehension. The Third Edition's presentation of the fundamentals of research design and data management is followed by five distinct methods of analysis: exploring, describing, ordering, explaining, and predicting. Miles and Huberman′s original research studies are profiled and accompanied with new examples from Salda�a′s recent qualitative work. The book′s most celebrated chapter, Drawing and Verifying Conclusions, is retained and revised, and the chapter on report writing has been greatly expanded, and is now called Writing About Qualitative Research. Comprehensive and authoritative, Qualitative Data Analysis has been elegantly revised for a new generation of qualitative researchers.