Python Feature Engineering Cookbook. A complete guide to crafting powerful features for your machine learning models - Third Edition

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.

The Machine Learning Solutions Architect Handbook. Create machine learning platforms to run solutions in an enterprise setting

David Ping

When equipped with a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization. There is a huge demand for skilled ML solutions architects in different industries, and this handbook will help you master the design patterns, architectural considerations, and the latest technology insights you’ll need to become one. You’ll start by understanding ML fundamentals and how ML can be applied to solve real-world business problems. Once you've explored a few leading problem-solving ML algorithms, this book will help you tackle data management and get the most out of ML libraries such as TensorFlow and PyTorch. Using open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines will be covered next, before moving on to building an enterprise ML architecture using Amazon Web Services (AWS). You’ll also learn about security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development. And finally, you'll get acquainted with AWS AI services and their applications in real-world use cases.By the end of this book, you’ll be able to design and build an ML platform to support common use cases and architecture patterns like a true professional.

Building Online Stores with osCommerce: Beginner Edition. A step by step introduction to osCommerce

David Mercer

Using an easy-to-read and engaging style, this book introduces the fundamentals of osCommerce, and helps you build your first online store. It covers the out-of-the-box features of osCommerce, but it also shows you how to customize the application to your own needs.The book starts with the basics of downloading and installing osCommerce, or simply how to enable it on your Internet domain using the tools provided by your host. All of the most important configuration issues are explained, with clear instructions and advice to help you make the right choices.Once osCommerce is installed and configured, you will take a good look at how to work with your store's data including product information as well as other data which is responsible for keeping your site healthy.The all important topic of customization is also dealt with comprehensively. You will see how to develop attractive sites that will make your store a pleasure to browse and your products a pleasure to buy!Of course, no discussion on osCommerce would be complete without a look at how to obtain and treat payments. Using the modules provided with osCommerce you will be collecting money from your happy customers in no time!Once the reader has a fully fledged, and operational site it is time to look at deployment? an important topic for discussion if development has taken place on a development machine.The appendix will add a few tools to your armory and shed some light as to what is going on behind the scenes in case things go awry.

CiviCRM Cookbook. Improve your CiviCRM capabilities with this clever cookbook. Packed with recipes and screenshots, it's the natural way to dig deeper into the software and achieve more for your nonprofit or civic sector organization

ANTHONY HORROCKS, Dave Greenberg

CiviCRM is a web-based, open source, Constituent Relationship Management (CRM) software geared toward meeting the needs of non-profit and other civic-sector organizations.Organizations realize their mission via CiviCRM through contact management, fundraising, event management, member management, mass e-mail marketing, peer-to-peer campaigns, case management, and much more.CiviCRM is localized in over 20 languages including: Chinese (Taiwan, China), Dutch, English (Australia, Canada, U.S., UK), French (France, Canada), German, Italian, Japanese, Russian, and Swedish.CiviCRM Cookbook will enhance your CiviCRM skills. It has recipes to help you use CiviCRM more efficiently, integrate it with CMSs, and also develop CiviCRM.This book begins with recipes that help save time and effort with CiviCRM. This is followed by recipes for organizing data more efficiently and managing profiles.Then you will learn authentication and authorization and managing communication with contacts.Then you will be guided on using the searching feature and preparing reports. We will then talk about integrating Drupal and CiviCRM. You will also be taught to manage events effectively. Finally, learn about CiviCampaign, Civimember, and developing CiviCRM.

Learning Cocos2d-x Game Development. Learn cross-platform game development with Cocos2d-x

Siddharth Shekar

Scala: Guide for Data Science Professionals. Build robust data pipelines with Scala

Arun Manivannan, Pascal Bugnion, Patrick R. Nicolas

Scala is especially good for analyzing large sets of data as the scale of the task doesn’t have any significant impact on performance. Scala’s powerful functional libraries can interact with databases and build scalable frameworks — resulting in the creation of robust data pipelines. The first module introduces you to Scala libraries to ingest, store, manipulate, process, and visualize data. Using real world examples, you will learn how to design scalable architecture to process and model data — starting from simple concurrency constructs and progressing to actor systems and Apache Spark. After this, you will also learn how to build interactive visualizations with web frameworks.Once you have become familiar with all the tasks involved in data science, you will explore data analytics with Scala in the second module. You’ll see how Scala can be used to make sense of data through easy to follow recipes. You will learn about Bokeh bindings for exploratory data analysis and quintessential machine learning with algorithms with Spark ML library. You’ll get a sufficient understanding of Spark streaming, machine learning for streaming data, and Spark graphX. Armed with a firm understanding of data analysis, you will be ready to explore the most cutting-edge aspect of data science — machine learning. The final module teaches you the A to Z of machine learning with Scala. You’ll explore Scala for dependency injections and implicits, which are used to write machine learning algorithms. You’ll also explore machine learning topics such as clustering, dimentionality reduction, Naïve Bayes, Regression models, SVMs, neural networks, and more. This learning path combines some of the best that Packt has to offer into one complete, curated package. It includes content from the following Packt products:• Scala for Data Science, Pascal Bugnion• Scala Data Analysis Cookbook, Arun Manivannan • Scala for Machine Learning, Patrick R. Nicolas

Big Data Analytics. Real time analytics using Apache Spark and Hadoop

Venkat Ankam

Big Data Analytics book aims at providing the fundamentals of Apache Spark and Hadoop. All Spark components – Spark Core, Spark SQL, DataFrames, Data sets, Conventional Streaming, Structured Streaming, MLlib, Graphx and Hadoop core components – HDFS, MapReduce and Yarn are explored in greater depth with implementation examples on Spark + Hadoop clusters. It is moving away from MapReduce to Spark. So, advantages of Spark over MapReduce are explained at great depth to reap benefits of in-memory speeds. DataFrames API, Data Sources API and new Data set API are explained for building Big Data analytical applications. Real-time data analytics using Spark Streaming with Apache Kafka and HBase is covered to help building streaming applications. New Structured streaming concept is explained with an IOT (Internet of Things) use case. Machine learning techniques are covered using MLLib, ML Pipelines and SparkR and Graph Analytics are covered with GraphX and GraphFrames components of Spark. Readers will also get an opportunity to get started with web based notebooks such as Jupyter, Apache Zeppelin and data flow tool Apache NiFi to analyze and visualize data.

Banana Pi Cookbook. Over 25 recipes to build projects and applications for multiple platforms with Banana Pi

Ryad El-Dajani