章节目录
Chapter 1Introduction The Ascendance of Data What Is Data Science? Motivating Hypothetical: DataSciencester Chapter 2A Crash Course in Python The Basics The Not-So-Basics For Further Exploration Chapter 3Visualizing Data matplotlib Bar Charts Line Charts Scatterplots For Further Exploration Chapter 4Linear Algebra Vectors Matrices For Further Exploration Chapter 5Statistics Describing a Single Set of Data Correlation Simpson’s Paradox Some Other Correlational Caveats Correlation and Causation For Further Exploration Chapter 6Probability Dependence and Independence Conditional Probability Bayes’s Theorem Random Variables Continuous Distributions The Normal Distribution The Central Limit Theorem For Further Exploration Chapter 7Hypothesis and Inference Statistical Hypothesis Testing Example: Flipping a Coin Confidence Intervals P-hacking Example: Running an A/B Test Bayesian Inference For Further Exploration Chapter 8Gradient Descent The Idea Behind Gradient Descent Estimating the Gradient Using the Gradient Choosing the Right Step Size Putting It All Together Stochastic Gradient Descent For Further Exploration Chapter 9Getting Data stdin and stdout Reading Files Scraping the Web Using APIs Example: Using the Twitter APIs For Further Exploration Chapter 10Working with Data Exploring Your Data Cleaning and Munging Manipulating Data Rescaling Dimensionality Reduction For Further Exploration Chapter 11Machine Learning Modeling What Is Machine Learning? Overfitting and Underfitting Correctness The Bias-Variance Trade-off Feature Extraction and Selection For Further Exploration Chapter 12k-Nearest Neighbors The Model Example: Favorite Languages The Curse of Dimensionality For Further Exploration Chapter 13Naive Bayes A Really Dumb Spam Filter A More Sophisticated Spam Filter Implementation Testing Our Model For Further Exploration Chapter 14Simple Linear Regression The Model Using Gradient Descent Maximum Likelihood Estimation For Further Exploration Chapter 15Multiple Regression The Model Further Assumptions of the Least Squares Model Fitting the Model Interpreting the Model Goodness of Fit Digression: The Bootstrap Standard Errors of Regression Coefficients Regularization For Further Exploration Chapter 16Logistic Regression The Problem The Logistic Function Applying the Model Goodness of Fit Support Vector Machines For Further Investigation Chapter 17Decision Trees What Is a Decision Tree? Entropy The Entropy of a Partition Creating a Decision Tree Putting It All Together Random Forests For Further Exploration Chapter 18Neural Networks Perceptrons Feed-Forward Neural Networks Backpropagation Example: Defeating a CAPTCHA For Further Exploration Chapter 19Clustering The Idea The Model Example: Meetups Choosing k Example: Clustering Colors Bottom-up Hierarchical Clustering For Further Exploration Chapter 20Natural Language Processing Word Clouds n-gram Models Grammars An Aside: Gibbs Sampling Topic Modeling For Further Exploration Chapter 21Network Analysis Betweenness Centrality Eigenvector Centrality Directed Graphs and PageRank For Further Exploration Chapter 22Recommender Systems Manual Curation Recommending What’s Popular User-Based Collaborative Filtering Item-Based Collaborative Filtering For Further Exploration Chapter 23Databases and SQL CREATE TABLE and INSERT UPDATE DELETE SELECT GROUP BY ORDER BY JOIN Subqueries Indexes Query Optimization NoSQL For Further Exploration Chapter 24MapReduce Example: Word Count Why MapReduce? MapReduce More Generally Example: Analyzing Status Updates Example: Matrix Multiplication An Aside: Combiners For Further Exploration Chapter 25Go Forth and Do Data Science IPython Mathematics Not from Scratch Find Data Do Data Science
内容简介
Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
下载说明
1、Data Science from Scratch是作者Joel Grus创作的原创作品，下载链接均为网友上传的网盘链接！
2、相识电子书提供优质免费的txt、pdf等下载链接，所有电子书均为完整版！