Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you'll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data

    This acclaimed book delivers beautifully illustrated information for learning palpation and the musculoskeletal system. It makes mastering the essential manual therapy skills interesting, memorable and easy. With 440 pages and 1,400 illustrations covering more than 162 muscles, 206 bones, 33 ligaments and 110 bony landmarks, this text provides an invaluable map of the body. A complimentary DVD for practicing palpation is included with the textbook.

    A who's who of pathology experts delivers the most dependable, current, and complete coverage of today's essential pathology knowledge. At the same time, masterful editing and a practical organization make mastering every concept remarkably easy. Online access via Student Consult includes self-assessment and review questions, interactive case studies, downloadable images, videos, and a virtual microscope that lets you view slides at different magnifications. The result remains the ideal source for an optimal understanding of pathology. Offers the most authoritative and comprehensive, yet readable coverage available in any pathology textbook, making it ideal for USMLE or specialty board preparation as well as for course work. Includes access to the complete contents online via Student Consult, along with self-assessment and review questions, over 100 interactive clinical case studies, videos, and a virtual microscope that lets users view slides at different magnifications.Delivers a state-of-the-art understanding of the pathologic basis of disease through completely updated coverage, including the latest cellular and molecular biology.Demonstrates every concept visually with over 1,600 full-color photomicrographs and conceptual diagrams - many revised for even better quality.Facilitates learning with an outstanding full-color, highly user-friendly design.

    It is suitable for a one or two semester university course on AI, as well as for researchers in the field.

    Includes new and extended problem sets in every chapter, all of which have been annotated for level and skill type Features three new chapters on advanced topics including vP shells, object shells, control, gapping and ellipsis and an additional chapter on advanced topics in binding Offers a brief survey of both Lexical-Functional Grammar and Head-Driven Phrase Structure Grammar Succeeds in strengthening the reader's foundational knowledge, and prepares them for more advanced study Supported by an instructor's manual and online resources for students and instructors, available at www.blackwellpublishing.com/carnie

    Without machine learning, fraud detection, web search results, real-time ads on web pages, credit scoring, automation, and email spam filtering wouldn't be possible, and this is only showcasing just a few of its capabilities. Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks.Covering the entry-level topics needed to get you familiar with the basic concepts of machine learning, this guide quickly helps you make sense of the programming languages and tools you need to turn machine learning-based tasks into a reality. Whether you're maddened by the math behind machine learning, apprehensive about AI, perplexed by preprocessing data--or anything in between--this guide makes it easier to understand and implement machine learning seamlessly.Grasp how day-to-day activities are powered by machine learning Learn to 'speak' certain languages, such as Python and R, to teach machines to perform pattern-oriented tasks and data analysis Learn to code in R using R Studio Find out how to code in Python using Anaconda Dive into this complete beginner's guide so you are armed with all you need to know about machine learning!

    We currently see no evidence of any kind indicating that extraterrestrials exist outside of our solar system. But at this moment, millions of engineers, scientists, corporations, universities and entrepreneurs are racing to create the second intelligent species right here on planet earth. And we can see the second intelligent species coming from all directions in the form of self-driving cars, automated call centers, chess-playing and Jeopardy-playing computers that beat all human players, airport kiosks, restaurant tablet systems, etc. The frightening thing is that these robots will soon be eliminating human jobs in startling numbers. The first wave of unemployed workers is likely to be a million truck drivers who are replaced by self-driving trucks. Pilots will be eliminated soon as well. Then, as new computer vision systems come online, we will see tens of millions of workers in retail stores, fast food restaurants and construction sites replaced by robots. Unless we take steps now to change the economy, we will soon have tens of millions of workers who are unemployed and seeking welfare because they will have no other choice. Marshall Brain's new book "The Second Intelligent Species: How Humans Will Become as Irrelevant as Cockroaches" explores how the future will unfold as the second intelligent species emerges. The book answers questions like: - How will new computer vision systems affect the job market? - How many people will become unemployed by the second intelligent species? - What will happen to millions of newly unemployed workers? - How can modern society and modern economies cope with run-away unemployment caused by robots? - What will happen when the first sentient, conscious computer appears? - What moral and ethical principles will guide the second intelligent species? - Why do we see no extraterrestrials in our universe? "The Second Intelligent Species" offers a unique and fascinating look at the future of the human race, and the choices we will need to make to avoid massive unemployment and poverty worldwide as intelligent machines start eliminating millions of jobs.

    Visually engaging, enticing and current examples with an overall focus on business.Legal Environment of Business and E-Commerce; Torts, Crimes, and Intellectual Property; Contracts and E-Commerce; Domestic and International Sales and Lease Contracts; Negotiable Instruments and E-Money; Credit, Secured Transactions, and Bankruptcy; Agency and Employment; Business Organizations and Ethics; Government Regulation; Property; Special Topics; Global EnvironmentMARKET Business Law continues its dedication to being the most engaging text for readers by featuring a visually appealing format with enticing and current examples while maintaining its focus on business.

    Welcome to laughing until it hurts while covered in bodily fluids. Welcome to simple math at very high stakes. Welcome to an incredibly inappropriate sense of humor. Welcome to serving people on the most stressful days of their lives. Welcome to putting your hands in places you never imagined they'd be. Welcome to your front row seat to the ballad of life and death. That's not the welcome that this nurse was looking for, but that's the one he got. Irreverent and audacious, this brutally honest memoir covers what it’s like to come of age in an American Hospital. Welcome to a rollicking peak behind the curtain to what medical providers, and the health care system, are truly like.

    The data compiled and portraits created are incredibly detailed, to the point of being invasive. But who connects the dots about what firms are doing with this information? The Black Box Society argues that we all need to be able to do so--and to set limits on how big data affects our lives.Hidden algorithms can make (or ruin) reputations, decide the destiny of entrepreneurs, or even devastate an entire economy. Shrouded in secrecy and complexity, decisions at major Silicon Valley and Wall Street firms were long assumed to be neutral and technical. But leaks, whistleblowers, and legal disputes have shed new light on automated judgment. Self-serving and reckless behavior is surprisingly common, and easy to hide in code protected by legal and real secrecy. Even after billions of dollars of fines have been levied, underfunded regulators may have only scratched the surface of this troubling behavior.Frank Pasquale exposes how powerful interests abuse secrecy for profit and explains ways to rein them in. Demanding transparency is only the first step. An intelligible society would assure that key decisions of its most important firms are fair, nondiscriminatory, and open to criticism. Silicon Valley and Wall Street need to accept as much accountability as they impose on others.

    Written by Magnus Lie Hetland, author of Beginning Python, this book is sharply focused on classical algorithms, but it also gives a solid understanding of fundamental algorithmic problem-solving techniques.The book deals with some of the most important and challenging areas of programming and computer science, but in a highly pedagogic and readable manner. The book covers both algorithmic theory and programming practice, demonstrating how theory is reflected in real Python programs. Well-known algorithms and data structures that are built into the Python language are explained, and the user is shown how to implement and evaluate others himself.

     Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work. This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also included. The ambition of this guide is to make neural networks as accessible as possible to as many readers as possible - there are enough texts for advanced readers already! You'll learn to code in Python and make your own neural network, teaching it to recognise human handwritten numbers, and performing as well as professionally developed networks. Part 1 is about ideas. We introduce the mathematical ideas underlying the neural networks, gently with lots of illustrations and examples. Part 2 is practical. We introduce the popular and easy to learn Python programming language, and gradually builds up a neural network which can learn to recognise human handwritten numbers, easily getting it to perform as well as networks made by professionals. Part 3 extends these ideas further. We push the performance of our neural network to an industry leading 98% using only simple ideas and code, test the network on your own handwriting, take a privileged peek inside the mysterious mind of a neural network, and even get it all working on a Raspberry Pi. All the code in this has been tested to work on a Raspberry Pi Zero.

    

    Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, recognize faces or spoken speech, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. "Introduction to Machine Learning" is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. It discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The book can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.After an introduction that defines machine learning and gives examples of machine learning applications, the book covers supervised learning, Bayesian decision theory, parametric methods, multivariate methods, dimensionality reduction, clustering, nonparametric methods, decision trees, linear discrimination, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, combining multiple learners, and reinforcement learning.

    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