Building on the successful top-down approach of previous editions, this fourth edition continues with an early emphasis on application-layer paradigms and application programming interfaces, encouraging a hands-on experience with protocols and networking concepts.

    Written by three prominent members of the Perl community who each have several years of experience teaching Perl around the world, this edition has been updated to account for all the recent changes to the language up to Perl 5.8.Perl is the language for people who want to get work done. It started as a tool for Unix system administrators who needed something powerful for small tasks. Since then, Perl has blossomed into a full-featured programming language used for web programming, database manipulation, XML processing, and system administration--on practically all platforms--while remaining the favorite tool for the small daily tasks it was designed for. You might start using Perl because you need it, but you'll continue to use it because you love it.Informed by their years of success at teaching Perl as consultants, the authors have re-engineered the Llama to better match the pace and scope appropriate for readers getting started with Perl, while retaining the detailed discussion, thorough examples, and eclectic wit for which the Llama is famous.The book includes new exercises and solutions so you can practice what you've learned while it's still fresh in your mind. Here are just some of the topics covered:Perl variable typessubroutinesfile operationsregular expressionstext processingstrings and sortingprocess managementusing third party modulesIf you ask Perl programmers today what book they relied on most when they were learning Perl, you'll find that an overwhelming majority will point to the Llama. With good reason. Other books may teach you to program in Perl, but this book will turn you into a Perl programmer.

    Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

    This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way. You’ll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

    Discover the “hidden gold” in Python’s standard library and start writing clean and Pythonic code today. Who Should Read This Book: If you’re wondering which lesser known parts in Python you should know about, you’ll get a roadmap with this book. Discover cool (yet practical!) Python tricks and blow your coworkers’ minds in your next code review. If you’ve got experience with legacy versions of Python, the book will get you up to speed with modern patterns and features introduced in Python 3 and backported to Python 2. If you’ve worked with other programming languages and you want to get up to speed with Python, you’ll pick up the idioms and practical tips you need to become a confident and effective Pythonista. If you want to make Python your own and learn how to write clean and Pythonic code, you’ll discover best practices and little-known tricks to round out your knowledge. What Python Developers Say About The Book: "I kept thinking that I wished I had access to a book like this when I started learning Python many years ago." — Mariatta Wijaya, Python Core Developer"This book makes you write better Python code!" — Bob Belderbos, Software Developer at Oracle"Far from being just a shallow collection of snippets, this book will leave the attentive reader with a deeper understanding of the inner workings of Python as well as an appreciation for its beauty." — Ben Felder, Pythonista"It's like having a seasoned tutor explaining, well, tricks!" — Daniel Meyer, Sr. Desktop Administrator at Tesla Inc.

    But if one per year is good, how about Seven Languages in Seven Weeks? In this book you'll get a hands-on tour of Clojure, Haskell, Io, Prolog, Scala, Erlang, and Ruby. Whether or not your favorite language is on that list, you'll broaden your perspective of programming by examining these languages side-by-side. You'll learn something new from each, and best of all, you'll learn how to learn a language quickly. Ruby, Io, Prolog, Scala, Erlang, Clojure, Haskell. With Seven Languages in Seven Weeks, by Bruce A. Tate, you'll go beyond the syntax-and beyond the 20-minute tutorial you'll find someplace online. This book has an audacious goal: to present a meaningful exploration of seven languages within a single book. Rather than serve as a complete reference or installation guide, Seven Languages hits what's essential and unique about each language. Moreover, this approach will help teach you how to grok new languages. For each language, you'll solve a nontrivial problem, using techniques that show off the language's most important features. As the book proceeds, you'll discover the strengths and weaknesses of the languages, while dissecting the process of learning languages quickly--for example, finding the typing and programming models, decision structures, and how you interact with them. Among this group of seven, you'll explore the most critical programming models of our time. Learn the dynamic typing that makes Ruby, Python, and Perl so flexible and compelling. Understand the underlying prototype system that's at the heart of JavaScript. See how pattern matching in Prolog shaped the development of Scala and Erlang. Discover how pure functional programming in Haskell is different from the Lisp family of languages, including Clojure. Explore the concurrency techniques that are quickly becoming the backbone of a new generation of Internet applications. Find out how to use Erlang's let-it-crash philosophy for building fault-tolerant systems. Understand the actor model that drives concurrency design in Io and Scala. Learn how Clojure uses versioning to solve some of the most difficult concurrency problems. It's all here, all in one place. Use the concepts from one language to find creative solutions in another-or discover a language that may become one of your favorites.

    The pytest testing framework helps you write tests quickly and keep them readable and maintainable - with no boilerplate code. Using a robust yet simple fixture model, it's just as easy to write small tests with pytest as it is to scale up to complex functional testing for applications, packages, and libraries. This book shows you how.For Python-based projects, pytest is the undeniable choice to test your code if you're looking for a full-featured, API-independent, flexible, and extensible testing framework. With a full-bodied fixture model that is unmatched in any other tool, the pytest framework gives you powerful features such as assert rewriting and plug-in capability - with no boilerplate code.With simple step-by-step instructions and sample code, this book gets you up to speed quickly on this easy-to-learn and robust tool. Write short, maintainable tests that elegantly express what you're testing. Add powerful testing features and still speed up test times by distributing tests across multiple processors and running tests in parallel. Use the built-in assert statements to reduce false test failures by separating setup and test failures. Test error conditions and corner cases with expected exception testing, and use one test to run many test cases with parameterized testing. Extend pytest with plugins, connect it to continuous integration systems, and use it in tandem with tox, mock, coverage, unittest, and doctest.Write simple, maintainable tests that elegantly express what you're testing and why.What You Need: The examples in this book are written using Python 3.6 and pytest 3.0. However, pytest 3.0 supports Python 2.6, 2.7, and Python 3.3-3.6.

    Mitnick claims that this socialengineering tactic was the single-most effective method in hisarsenal. This indispensable book examines a variety of maneuversthat are aimed at deceiving unsuspecting victims, while it alsoaddresses ways to prevent social engineering threats.Examines social engineering, the science of influencing atarget to perform a desired task or divulge informationArms you with invaluable information about the many methods oftrickery that hackers use in order to gather information with theintent of executing identity theft, fraud, or gaining computersystem accessReveals vital steps for preventing social engineeringthreatsSocial Engineering: The Art of Human Hacking does itspart to prepare you against nefarious hackers--now you can doyour part by putting to good use the critical information withinits pages.

    Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

    It's Tuesday morning and on his drive into the office, Bill gets a call from the CEO. The company's new IT initiative, code named Phoenix Project, is critical to the future of Parts Unlimited, but the project is massively over budget and very late. The CEO wants Bill to report directly to him and fix the mess in ninety days or else Bill's entire department will be outsourced. With the help of a prospective board member and his mysterious philosophy of The Three Ways, Bill starts to see that IT work has more in common with manufacturing plant work than he ever imagined. With the clock ticking, Bill must organize work flow streamline interdepartmental communications, and effectively serve the other business functions at Parts Unlimited. In a fast-paced and entertaining style, three luminaries of the DevOps movement deliver a story that anyone who works in IT will recognize. Readers will not only learn how to improve their own IT organizations, they'll never view IT the same way again.

    This is the undisputed go-to guide to Rust, written by two members of the Rust core team, with feedback and contributions from 42 members of the community. The book assumes that you’ve written code in another programming language but makes no assumptions about which one, meaning the material is accessible and useful to developers from a wide variety of programming backgrounds.Known by the Rust community as "The Book," The Rust Programming Language includes concept chapters, where you’ll learn about a particular aspect of Rust, and project chapters, where you’ll apply what you’ve learned so far to build small programs.The Book opens with a quick hands-on project to introduce the basics then explores key concepts in depth, such as ownership, the type system, error handling, and fearless concurrency. Next come detailed explanations of Rust-oriented perspectives on topics like pattern matching, iterators, and smart pointers, with concrete examples and exercises--taking you from theory to practice.The Rust Programming Language will show you how to: Grasp important concepts unique to Rust like ownership, borrowing, and lifetimes Use Cargo, Rust’s built-in package manager, to build and maintain your code, including downloading and building dependencies Effectively use Rust’s zero-cost abstractions and employ your ownYou’ll learn to develop reliable code that’s speed and memory efficient, while avoiding the infamous and arcane programming pitfalls common at the systems level. When you need to dive down into lower-level control, this guide will show you how without taking on the customary risk of crashes or security holes and without requiring you to learn the fine points of a fickle toolchain.You’ll also learn how to create command line programs, build single- and multithreaded web servers, and much more.The Rust Programming Language fully embraces Rust’s potential to empower its users. This friendly and approachable guide will help you build not only your knowledge of Rust but also your ability to program with confidence in a wider variety of domains.

    Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of their organization’s massive data sets and applying their findings to real-world business scenarios. From uncovering rich data sources to managing large amounts of data within hardware and software limitations, ensuring consistency in reporting, merging various data sources, and beyond, you’ll develop the know-how you need to effectively interpret data and tell a story that can be understood by anyone in your organization. Provides a background in data science fundamentals before moving on to working with relational databases and unstructured data and preparing your data for analysis Details different data visualization techniques that can be used to showcase and summarize your data Explains both supervised and unsupervised machine learning, including regression, model validation, and clustering techniques Includes coverage of big data processing tools like MapReduce, Hadoop, Dremel, Storm, and Spark It’s a big, big data world out there – let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.

    This Box Set Includes 3 Books: Python Programming For Beginners - Learn The Basics Of Python In 7 Days! Python Programming For Intermediates - Learn The Basics Of Python In 7 Days! Python Programming For Advanced - Learn The Basics Of Python In 7 Days! Python Programming For Beginners - Learn The Basics Of Python In 7 Days! Here's what you'll learn from this book: ✓Introduction ✓Understanding Python: A Detailed Background ✓How Python Works ✓Python Glossary ✓How to Download and Install Python ✓Python Programming 101: Interacting With Python in Different Ways ✓How to Write Your First Python Program ✓Variables, Strings, Lists, Tuples, Dictionaries ✓About User-Defined Functions ✓How to Write User-Defined Functions in Python ✓About Coding Style ✓Practice Projects: The Python Projects for Your Practice Python Programming For Intermediates - Learn The Basics Of Python In 7 Days! Here's what you'll learn from this book: ✓ Shallow copy and deep copy ✓ Objects and classes in Python–including python inheritance, multiple inheritances, and so on ✓ Recursion in Python ✓ Debugging and testing ✓ Fibonacci sequence (definition) and Memoization in Python in Python ✓ Arguments in Python ✓ Namespaces in Python and Python Modules ✓ Simple Python projects for Intermediates Python Programming For Advanced - Learn The Basics Of Python In 7 Days! Here's what you'll learn from this book: ✓File management ✓Python Iterator ✓Python Generator ✓Regular Expressions ✓Python Closure ✓Python Property ✓Python Assert, and ✓Simple recap projects Start Coding Now!

    The aim of The Node Beginner Book is to get you started with developing applications for Node.js, teaching you everything you need to know about advanced JavaScript along the way on 59 pages.

    Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data.Each chapter will cover a different technique in a spreadsheet so you can follow along: - Mathematical optimization, including non-linear programming and genetic algorithms- Clustering via k-means, spherical k-means, and graph modularity- Data mining in graphs, such as outlier detection- Supervised AI through logistic regression, ensemble models, and bag-of-words models- Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation- Moving from spreadsheets into the R programming languageYou get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.