If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.With this book, you'll learn:Fundamental concepts and applications of machine learningAdvantages and shortcomings of widely used machine learning algorithmsHow to represent data processed by machine learning, including which data aspects to focus onAdvanced methods for model evaluation and parameter tuningThe concept of pipelines for chaining models and encapsulating your workflowMethods for working with text data, including text-specific processing techniquesSuggestions for improving your machine learning and data science skills

    This book will teach you how to use bash's advanced command-line features, such as command history, command-line editing, and command completion.This book also introduces shell programming,a skill no UNIX or Linus user should be without. The book demonstrates what you can do with bash's programming features. You'll learn about flow control, signal handling, and command-line processing and I/O. There is also a chapter on debugging your bash programs.Finally, Learning the bash Shell, Third Edition, shows you how to acquire, install, configure, and customize bash, and gives advice to system administrators managing bash for their user communities.This Third Edition covers all of the features of bash Version 3.0, while still applying to Versions 1.x and 2.x. It includes a debugger for the bash shell, both as an extended example and as a useful piece of working code. Since shell scripts are a significant part of many software projects, the book also discusses how to write maintainable shell scripts. And, of course, it discusses the many features that have been introduced to bash over the years: one-dimensional arrays, parameter expansion, pattern-matching operations, new commands, and security improvements.Unfailingly practical and packed with examples and questions for future study, Learning the bash Shell Third Edition is a valuable asset for Linux and other UNIX users.--back cover

    Python is easy to learn, and it's perfect for exploring topics like statistics, geometry, probability, and calculus. You’ll learn to write programs to find derivatives, solve equations graphically, manipulate algebraic expressions, even examine projectile motion.Rather than crank through tedious calculations by hand, you'll learn how to use Python functions and modules to handle the number crunching while you focus on the principles behind the math. Exercises throughout teach fundamental programming concepts, like using functions, handling user input, and reading and manipulating data. As you learn to think computationally, you'll discover new ways to explore and think about math, and gain valuable programming skills that you can use to continue your study of math and computer science.If you’re interested in math but have yet to dip into programming, you’ll find that Python makes it easy to go deeper into the subject—let Python handle the tedious work while you spend more time on the math.

    Fad diets all promise miraculous results for your outward appearance—yet people continue to eat poorly, gain weight, and depend on medications and operations to maintain their health. Learn the secrets of intuitive eating and start building a new relationship with your body. Integrative Nutrition is loaded with valuable insights into nutritional theories, simple ways to nurture your body, and holistic approaches to maximize health. Integrative Nutrition offers a play-by-play for proper nutrition and personal growth, and is packed with delicious, easy-to-follow recipes. What Integrative Nutrition can do for you: •Learn the truth about food corporations, ­pharmaceutical companies, and obesity •Weigh the strengths and weaknesses of many popular diets and cleanses •Discover why your body craves certain foods and why you should listen to those cravings •Explore the connection between food, ­sexuality, spirituality, and work •Find out how cooking at home can boost your health •Add more to your diet rather than cut back •Release your dependency on restaurant food, fast food, and processed food •Don't be a health food addict: enjoy your favorite foods without guilt

    Harper focuses on nutrient-dense foods that are big in flavor and allow certain kinds of carbohydrates at targeted times during the day.In The Super Carb Diet you'll find: - How to eat carbs earlier in the day for sustained energy- A list of super-carb foods- Limited snacks but larger and more varied meals- A way of eating that's sustainable- Super-charged weight lossThe Super Carb Diet will keep millions of dieters from giving up after Week One. The program leads you through precise plate proportions, balancing good protein, low fat, high fiber, and nutrient density. Not only will you lose significant weight and whittle your waistline, you'll walk away from the table feeling happy and full.

    The language concepts are aptly explained in simple and easy-to-understand style, supported with examples, illustrations and programming and debugging exercises.

    Five years ago, in What is Web 2.0, Tim O'Reilly said that "data is the next Intel Inside." But what does that statement mean? Why do we suddenly care about statistics and about data? This report examines the many sides of data science -- the technologies, the companies and the unique skill sets.The web is full of "data-driven apps." Almost any e-commerce application is a data-driven application. There's a database behind a web front end, and middleware that talks to a number of other databases and data services (credit card processing companies, banks, and so on). But merely using data isn't really what we mean by "data science." A data application acquires its value from the data itself, and creates more data as a result. It's not just an application with data; it's a data product. Data science enables the creation of data products.

    With the 97 short and extremely useful tips for programmers in this book, you'll expand your skills by adopting new approaches to old problems, learning appropriate best practices, and honing your craft through sound advice.With contributions from some of the most experienced and respected practitioners in the industry--including Michael Feathers, Pete Goodliffe, Diomidis Spinellis, Cay Horstmann, Verity Stob, and many more--this book contains practical knowledge and principles that you can apply to all kinds of projects.A few of the 97 things you should know:"Code in the Language of the Domain" by Dan North"Write Tests for People" by Gerard Meszaros"Convenience Is Not an -ility" by Gregor Hohpe"Know Your IDE" by Heinz Kabutz"A Message to the Future" by Linda Rising"The Boy Scout Rule" by Robert C. Martin (Uncle Bob)"Beware the Share" by Udi Dahan

    But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications.Authors Jeremy Howard and Sylvain Gugger show you how to train a model on a wide range of tasks using fastai and PyTorch. You'll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes.Train models in computer vision, natural language processing, tabular data, and collaborative filteringLearn the latest deep learning techniques that matter most in practiceImprove accuracy, speed, and reliability by understanding how deep learning models workDiscover how to turn your models into web applicationsImplement deep learning algorithms from scratchConsider the ethical implications of your work

    You can think of Python as your tool to solve problems that are far beyond the capability of a spreadsheet. It is an easy-to-use and easy-to learn programming language that is freely available on Windows, Macintosh, and Linux computers. There are free downloadable copies of this book in various electronic formats and a self-paced free online course where you can explore the course materials. All the supporting materials for the book are available under open and remixable licenses. This book is designed to teach people to program even if they have no prior experience.

    Factor in nearly as many years of teaching experience, and that adds up to a whole lot of know-how to share. In Pastel Pointers, he lays it all out: information on tools, materials, color, composition, landscape elements, finishes and more. Compiles the best of McKinley's popular Pastel Pointers blog and Pastel Journal columns Covers frequently asked questions ("How do I achieve natural-looking greens?") and simple solutions to common problems, such as excess pigment buildup Includes a chapter on "The Business of Pastels"—tips for framing, shipping, preparing for gallery shows, and otherwise representing your work in a professional manner This book covers everything from the fundamentals to get you going (how to lay out your palette, create an underpainting, evoke luminous effects) to inspirations that will keep you growing (plein air painting, working in a series, keeping a painting journal). Whether you're a beginner or an experienced painter anxious to explore the expressive possibilities of pastel, this is your guide to making the most of the medium.

    Multicore machines and 64-bit operating systems are now standard even for casual users, and Java itself has introduced new features to manage applications. The base JVM has kept pace with those developments and offers a very different performance profile in its current versions. By guiding you through this changing landscape, Java Performance: The Definitive Guide helps you gain the best performance from your Java applications.You’ll explore JVM features that traditionally affected performance—including the just-in-time compiler, garbage collection, and language features—before diving in to aspects of Java 7 and 8 designed for maximum performance in today's applications. You’ll learn features such as the G1 garbage collector to maximize your application’s throughput without causing it to pause, and the Java Flight Recorder, which enables you to see application performance details without the need for separate, specialized profiling tools.Whether you’re new to Java and need to understand the basics of tuning the JVM, or a seasoned developer looking to eek out that last 10% of application performance, this is the book you want.

    With this hands-on guide, you'll learn how to write effective, idiomatic Python code by leveraging its best and possibly most neglected features. Author Luciano Ramalho takes you through Python's core language features and libraries, and shows you how to make your code shorter, faster, and more readable at the same time.Many experienced programmers try to bend Python to fit patterns they learned from other languages, and never discover Python features outside of their experience. With this book, those Python programmers will thoroughly learn how to become proficient in Python 3.This book covers:Python data model: understand how special methods are the key to the consistent behavior of objectsData structures: take full advantage of built-in types, and understand the text vs bytes duality in the Unicode ageFunctions as objects: view Python functions as first-class objects, and understand how this affects popular design patternsObject-oriented idioms: build classes by learning about references, mutability, interfaces, operator overloading, and multiple inheritanceControl flow: leverage context managers, generators, coroutines, and concurrency with the concurrent.futures and asyncio packagesMetaprogramming: understand how properties, attribute descriptors, class decorators, and metaclasses work"

    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.