Matt Harrison – författare

With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project.

Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You’ll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics.

This pocket reference includes sections that cover:

Classification, using the Titanic datasetCleaning data and dealing with missing dataExploratory data analysisCommon preprocessing steps using sample dataSelecting features useful to the modelModel selectionMetrics and classification evaluationRegression examples using k-nearest neighbor, decision trees, boosting, and moreMetrics for regression evaluationClusteringDimensionality reductionScikit-learn pipelines

With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project.

Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You’ll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics.

This pocket reference includes sections that cover:

Classification, using the Titanic datasetCleaning data and dealing with missing dataExploratory data analysisCommon preprocessing steps using sample dataSelecting features useful to the modelModel selectionMetrics and classification evaluationRegression examples using k-nearest neighbor, decision trees, boosting, and moreMetrics for regression evaluationClusteringDimensionality reductionScikit-learn pipelines

With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project.Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You’ll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics.This pocket reference includes sections that cover:Classification, using the Titanic datasetCleaning data and dealing with missing dataExploratory data analysisCommon preprocessing steps using sample dataSelecting features useful to the modelModel selectionMetrics and classification evaluationRegression examples using k-nearest neighbor, decision trees, boosting, and moreMetrics for regression evaluationClusteringDimensionality reductionScikit-learn pipelines

SummaryHapi.js in Action teaches you how to build modern Node-driven applications using hapi.js. Packed with examples, this book takes you from your first simple server through the skills you''ll need to build a complete application. Foreword by Eran Hammer.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the TechnologyThe hapi.js web framework for Node.js is built around three radical ideas. Developer productivity: hapi''s intuitive setup gets you up and running in no time. Maintainability: hapi''s modular design allows for easy scaling and improvement. Flexibility: hapi has a lightweight core that you can expand and customize with plugins. Are you ready to get hapi?About the BookHapi.js in Action is an incredibly practical book that teaches you to build APIs, servers, and applications using Node.js and the hapi.js framework. You''ll begin with an easy-to-follow mental model of a Node.js web application and see exactly where hapi fits into the picture. Then, you''ll walk through building an API and composing it into a web application. Along the way, you''ll explore key topics like validation, testing, authentication, and deployment. Throughout, you''ll learn how to build rock-solid, secure, and fast applications.What''s InsideBest practices for Node application designBuilding APIs with hapiMaintaining and scaling applicationsExtending hapi with pluginsAbout the ReaderThis book assumes you have strong JavaScript skills. Experience with Node.js is helpful but not required.About the AuthorMatt Harrison is a core contributor to hapi.js and an active member of the Node community.Table of Contents

PART 1 - FIRST STEPS

Introducing hapiBuilding an APIBuilding a website

PART 2 - EXPANDING YOUR TOOLBOX

Routes and handlers in-depthUnderstanding requests and responsesValidation with JoiBuilding modular applications with pluginsCache me if you can

PART 3 - CREATING ROCK-SOLID APPS

Authentication and securityTesting with Lab, Code, and server.inject()Production and beyond

Use the power of pandas to solve most complex scientific computing problems with ease. Revised for pandas 1.x.

Key Features

This is the first book on pandas 1.xPractical, easy to implement recipes for quick solutions to common problems in data using pandasMaster the fundamentals of pandas to quickly begin exploring any dataset

Book Description

The pandas library is massive, and it''s common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands as one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through situations that you are highly likely to encounter.This new updated and revised edition provides you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way. Many advanced recipes combine several different features across the pandas library to generate results.

What you will learn

Master data exploration in pandas through dozens of practice problemsGroup, aggregate, transform, reshape, and filter dataMerge data from different sources through pandas SQL-like operationsCreate visualizations via pandas hooks to matplotlib and seabornUse pandas, time series functionality to perform powerful analysesImport, clean, and prepare real-world datasets for machine learningCreate workflows for processing big data that doesn’t fit in memory

Who this book is for

This book is for Python developers, data scientists, engineers, and analysts. Pandas is the ideal tool for manipulating structured data with Python and this book provides ample instruction and examples. Not only does it cover the basics required to be proficient, but it goes into the details of idiomatic pandas.