Python Data Science Handbook Essential Tools for Working with Data

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My name is Frank Kane. I spent nine years at amazon.com and imdb.com, wrangling millions of customer ratings and customer transactions to produce things such as personalized recommendations for movies and products and "people who bought this also bought." I tell you, I wish we had Apache Spark back

《Science》杂志-机器学习究竟将如何影响人类未来的工作 - 中文版 + 英文原版，帮助大家对于机器学习最新的发展趋势进行了解

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Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you’ll feel confident about asking—and answering—complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas. With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you’ll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You’ll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means.

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Contents Preface xv Prologue: A machine learning sampler 1 1 The ingredients of machine learning 13 1.1 Tasks: the problems that can be solved with machine learning . . . . . . . 14 Looking for structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Evaluating performance on a task . . . . . . . . . . . . . . . . . . . . . . . . 18 1.2 Models: the output of machine learning . . . . . . . . . . . . . . . . . . . . 20 Geometric models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Probabilistic models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Logical models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Grouping and grading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 1.3 Features: the workhorses of machine learning . . . . . . . . . . . . . . . . 38 Two uses of features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 Feature construction and transformation . . . . . . . . . . . . . . . . . . . 41 Interaction between features . . . . . . . . . . . . . . . . . . . . . . . . . . 44 1.4 Summary and outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 What you’ll find in the rest of the book . . . . . . . . . . . . . . . . . . . . . 48 2 Binary classification and related tasks 49 2.1 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 ixx Contents Assessing classification performance . . . . . . . . . . . . . . . . . . . . . . 53 Visualising classification performance . . . . . . . . . . . . . . . . . . . . . 58 2.2 Scoring and ranking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 Assessing and visualising ranking performance . . . . . . . . . . . . . . . . 63 Turning rankers into classifiers . . . . . . . . . . . . . . . . . . . . . . . . . 69 2.3 Class probability estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 Assessing class probability estimates . .