Rejecting the idea that we should live in fear, suffer silently, or medicate ourselves unnecessarily through this hormonal shift, Mathews set out to get answers and advice from the medical establishment, alternative therapists, and her many friends in the midst of "the change." When she launched the Megs Menopause website, it quickly became the trending online destination for pre- and menopausal women all over the world.Now, in The New Hot, Mathews offers the results of all her research and discussions: the latest information about hormone treatments (hormone replacement therapy and bioidentical hormone therapy), her best tips and techniques for coping with menopausal symptoms (there are officially thirty-four possible symptoms; Mathews has dealt with thirty-two!), and dishy, girlfriend-to-girlfriend advice about what to really expect when you're aging. Entertaining, stylish, and informative, The New Hot will be the resource women everywhere are talking about, learning from, and recommending to one another.

    This book focuses on practical algorithms that have been used to solve key problems in data mining and which can be used on even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. The PageRank idea and related tricks for organizing the Web are covered next. Other chapters cover the problems of finding frequent itemsets and clustering. The final chapters cover two applications: recommendation systems and Web advertising, each vital in e-commerce. Written by two authorities in database and Web technologies, this book is essential reading for students and practitioners alike.

    Sold in over 85 countries and translated into more than 5 languages.---------------Want to get started on data science?Our promise: no math added.This book has been written in layman's terms as a gentle introduction to data science and its algorithms. Each algorithm has its own dedicated chapter that explains how it works, and shows an example of a real-world application. To help you grasp key concepts, we stick to intuitive explanations and visuals.Popular concepts covered include:- A/B Testing- Anomaly Detection- Association Rules- Clustering- Decision Trees and Random Forests- Regression Analysis- Social Network Analysis- Neural NetworksFeatures:- Intuitive explanations and visuals- Real-world applications to illustrate each algorithm- Point summaries at the end of each chapter- Reference sheets comparing the pros and cons of algorithms- Glossary list of commonly-used termsWith this book, we hope to give you a practical understanding of data science, so that you, too, can leverage its strengths in making better decisions.

    This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.

    As was the case with Gore's first movie (An Inconvenient Truth), the movie is bursting with bad science, bad policy and some outright falsehoods. The storm events Gore addresses occur naturally, and there is little or no evidence they are being made worse from human activities: sea level is rising at the same rate it was before humans started burning fossil fuels; in Miami Beach the natural rise is magnified because buildings and streets were constructed on reclaimed swampland that has been sinking; the 9/11 memorial was not flooded by sea level rise from melting ice sheets, but a storm surge at high tide, which would have happened anyway and was not predicted by Gore in his first movie, as he claims; the Greenland ice sheet undergoes melt every summer, which was large in 2012 but then unusually weak in 2017; glaciers advance and retreat naturally, as evidenced by 1,000 to 2,000 year old tree stumps being uncovered in Alaska; rain gauge measurements reveal the conflict in Syria was not caused by reduced rainfall hurting farming there, and in fact the Middle East is greening from increasing CO2 in the atmosphere; agricultural yields in China have been rising, not falling as claimed by Gore. The renewable energy sources touted by Gore (wind and solar), while a laudable goal for our future, are currently very expensive: their federal subsidies per kilowatt-hour of energy produced are huge compared to coal, natural gas, and nuclear power. These costs are hidden from the public in increased federal and state tax rates. Gore is correct that "it is right to save humanity", but what we might need saving from the most are bad decisions that reduce prosperity and hurt the poor.

    But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like nonparametric curve estimation, bootstrapping, and clas- sification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analyzing data. For some time, statistics research was con- ducted in statistics departments while data mining and machine learning re- search was conducted in computer science departments. Statisticians thought that computer scientists were reinventing the wheel. Computer scientists thought that statistical theory didn't apply to their problems. Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algo- rithms are more scalable than statisticians ever thought possible. Formal sta- tistical theory is more pervasive than computer scientists had realized.

    

    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

    The gear you select and how you use it can mean big results—or bigger disappointment.FASTER takes a scientific look at triathlon to see what truly makes you faster—and busts the myths and doublespeak that waste your money and race times. In this fascinating exploration of the forces at play in the swim-bike-run sport, astronautical engineer and triathlete Jim Gourley shows where to find free speed, speed on a budget, and the gear upgrades that are worth it.FASTER offers specific, science-based guidance on the fastest techniques and the most effective gear, answering questions like: •    Which wetsuit is best for me? •    What’s the best way to draft a swimmer? •    Should I buy a lighter bike? •    Deep dish or disc wheels? •    Are lighter shoes faster? •    Who’s right about running technique? Gourley reviews published studies in peer-reviewed journals to show what scientists have learned about swim drafting, pacing the bike leg, race strategy for short and long-course racing, and the fastest ways to handle transitions.FASTER will change how you think about your body, your gear, and the world around you. With science on your side, you'll make the smart calls that will make you a better, faster triathlete.

    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.

    It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you'll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language.Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It's ideal for analysts new to Python and for Python programmers new to scientific computing.Use the IPython interactive shell as your primary development environmentLearn basic and advanced NumPy (Numerical Python) featuresGet started with data analysis tools in the pandas libraryUse high-performance tools to load, clean, transform, merge, and reshape dataCreate scatter plots and static or interactive visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsMeasure data by points in time, whether it's specific instances, fixed periods, or intervalsLearn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples

    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.

    

    As recently as 1918, a pandemic of influenza claimed over 50 million lives worldwide. The advent of drugs and vaccines led to an era of hope when we thought our battles with infectious disease were won, but our optimism has been eroded by the recognition that many pathogens have the capacity to transform themselves and escape our efforts to eradicate them. Are we now facing an inevitable repeat of a calamity such as the 1918 influenza pandemic or the Black Death? Can we anticipate and thwart such an event, or are we wilfully creating the conditions that would promote the emergence of new and highly virulent human infectious disease?Sunetra Gupta is Professor of Theoretical Epidemiology at the University of Oxford specialising in infectious diseases. She holds a bachelor's degree from Princeton University and a Ph.D. from the University of London. She has been awarded the Scientific Medal by the Zoological Society of London and the Royal Society Rosalind Franklin Award for her scientific research. She is also a novelist whose books have been awarded the Sahitya Akademi Award, the Southern Arts Literature Prize, shortlisted for the Crossword Award, and longlisted for the DSC and Orange Prizes.

    The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo MCMC. Additional application areas explored include genetics, medicine, computer science, and information theory. The print book version includes a code that provides free access to an eBook version. The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout, they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces. The book includes many intuitive explanations, diagrams, and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R, a free statistical software environment.