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The Kubernetes Book. Navigate the world of Kubernetes with expertise - Second Edition
Nigel Poulton, Pushkar Joglekar
Containers transformed how we package and deploy applications, but they came with inherent challenges. Kubernetes is a platform for deploying and managing containerized applications at scale. It also abstracts underlying infrastructure, making it easier to deploy and manage applications across heterogenous clouds and on-premises environments. The Kubernetes Book offers a beginner-friendly approach to mastering Kubernetes architecture and core concepts such as Pods, Deployments, Services, StatefulSets, Ingress, & ConfigMaps. It also demystifies the Kubernetes API, teaches you how to secure Kubernetes, and brings you up to speed with important cloud-native concepts such as microservices, declarative configuration, desired state, & reconciliation. The book also serves as a comprehensive guide for software developers, systems administrators, cloud engineers, and architects to help them master Kubernetes. It offers the most precise explanations and is full of practical examples to help develop applications on a laptop and then deploy them to real-world Kubernetes environments. The Kubernetes Book is updated annually, meaning it's always up to date with the latest and greatest in the Kubernetes and cloud-native ecosystems.
Soledad Galli, Christoph Molnar
Streamline data preprocessing and feature engineering in your machine learning project with this third edition of the Python Feature Engineering Cookbook to make your data preparation more efficient.This guide addresses common challenges, such as imputing missing values and encoding categorical variables using practical solutions and open source Python libraries. You’ll learn advanced techniques for transforming numerical variables, discretizing variables, and dealing with outliers. Each chapter offers step-by-step instructions and real-world examples, helping you understand when and how to apply various transformations for well-prepared data.The book explores feature extraction from complex data types such as dates, times, and text. You’ll see how to create new features through mathematical operations and decision trees and use advanced tools like Featuretools and tsfresh to extract features from relational data and time series.By the end, you’ll be ready to build reproducible feature engineering pipelines that can be easily deployed into production, optimizing data preprocessing workflows and enhancing machine learning model performance.
Fabrizio Romano, Heinrich Kruger
Learn Python Programming, Fourth Edition, provides a comprehensive, up-to-date introduction to Python programming, covering fundamental concepts and practical applications. This edition has been meticulously updated to include the latest features from Python versions 3.9 to 3.12, new chapters on type hinting and CLI applications, and updated examples reflecting modern Python web development practices. This Python book empowers you to take ownership of writing your software and become independent in fetching the resources you need. By the end of this book, you will have a clear idea of where to go and how to build on what you have learned from the book.Through examples, the book explores a wide range of applications and concludes by building real-world Python projects based on the concepts you have learned. This Python book offers a clear and practical guide to mastering Python and applying it effectively in various domains, such as data science, web development, and automation.