Mastering Linux Shell Scripting. Master the complexities of Bash shell scripting and unlock the power of shell for your enterprise

Andrew Mallett

Shell scripting is a quick method to prototype a complex application or a problem by automating tasks when working on Linux-based systems. Using both simple one-line commands and command sequences complex problems can be solved with ease, from text processing to backing up sysadmin tools.In this book, you’ll discover everything you need to know to master shell scripting and make informed choices about the elements you employ. Get to grips with the fundamentals of creating and running a script in normal mode, and in debug mode. Learn about various conditional statements' code snippets, and realize the power of repetition and loops in your shell script. Implement functions and edit files using the Stream Editor, script in Perl, program in Python – as well as complete coverage of other scripting languages to ensure you can choose the best tool for your project.

Interpretable Machine Learning with Python. Learn to build interpretable high-performance models with hands-on real-world examples

Serg Masís

Do you want to gain a deeper understanding of your models and better mitigate poor prediction risks associated with machine learning interpretation? If so, then Interpretable Machine Learning with Python deserves a place on your bookshelf.We’ll be starting off with the fundamentals of interpretability, its relevance in business, and exploring its key aspects and challenges. As you progress through the chapters, you'll then focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. You’ll also get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, this book will also help you interpret model outcomes using examples. You’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you’ll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining.By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning.

Data Exploration and Preparation with BigQuery. A practical guide to cleaning, transforming, and analyzing data for business insights

Mike Kahn

Data professionals encounter a multitude of challenges such as handling large volumes of data, dealing with data silos, and the lack of appropriate tools. Datasets often arrive in different conditions and formats, demanding considerable time from analysts, engineers, and scientists to process and uncover insights. The complexity of the data life cycle often hinders teams and organizations from extracting the desired value from their data assets. Data Exploration and Preparation with BigQuery offers a holistic solution to these challenges.The book begins with the basics of BigQuery while covering the fundamentals of data exploration and preparation. It then progresses to demonstrate how to use BigQuery for these tasks and explores the array of big data tools at your disposal within the Google Cloud ecosystem.The book doesn’t merely offer theoretical insights; it’s a hands-on companion that walks you through properly structuring your tables for query efficiency and ensures adherence to data preparation best practices. You’ll also learn when to use Dataflow, BigQuery, and Dataprep for ETL and ELT workflows. The book will skillfully guide you through various case studies, demonstrating how BigQuery can be used to solve real-world data problems.By the end of this book, you’ll have mastered the use of SQL to explore and prepare datasets in BigQuery, unlocking deeper insights from data.

SAP HANA Cookbook. Your all-inclusive guide to understanding SAP HANA with practical recipes with over 50 recipes

Chandrasekhar Mankala (USD), Ganesh Mahadevan V., Ganesh Mahadevan (USD)

SAP HANA is a real-time applications platform that provides a multi-purpose, in-memory appliance. Decision makers in the organization can gain instant insight into business operations. Thus all the data available can be analysed and you can react to the changing business conditions rapidly to make decisions. The real-time platform not only empowers business users and top management to make decisions but also provides the capability to make decisions in real-time.A practical and comprehensive guide that helps you understand the power of SAP HANA’s real-time and in-memory capabilities. It also provides step-by-step instructions to exploit all the possible features of the SAP HANA database, enabling users to harness the full potential of this technology and its features.You will gain an understanding of real-time replications, effective data loading from various sources, how to load data, and how to create re-usable objects such as models and reports.Use this practical guide to enable or transform your business landscape by implementing SAP HANA to meet your business requirements. The book shows you how to load data from different types of systems, create models in SAP HANA, and consume data for decision-making. The book covers various tools at different stages creating models using SAP HANA Studio, and consuming data using reporting tools such as SAP BusinessObjects, SAP Lumira, and so on . This book also explains the in-depth architecture of SAP HANA to help you understand SAP HANA as an appliance, that is, a combination of hardware and software.The book covers the best practices to leverage SAP HANA’s in-memory technology to transform data into insightful information. It also covers technology landscaping, solution architecture, connectivity, data loading, and setting up the environment for modeling purpose (including setup of SAP HANA Studio).If you have an intention to start your career as SAP HANA Modeler, this book is the perfect start. 

Before Machine Learning Volume 1 - Linear Algebra for A.I. The Fundamental Mathematics for Data Science and Artificial Intelligence

Jorge Brasil

In this book, you'll embark on a comprehensive journey through the fundamentals of linear algebra, a critical component for any aspiring machine learning expert. Starting with an introductory overview, the course explains why linear algebra is indispensable for machine learning, setting the stage for deeper exploration. You'll then dive into the concepts of vectors and matrices, understanding their definitions, properties, and practical applications in the field.As you progress, the course takes a closer look at matrix decomposition, breaking down complex matrices into simpler, more manageable forms. This section emphasizes the importance of decomposition techniques in simplifying computations and enhancing data analysis. The final chapter focuses on principal component analysis, a powerful technique for dimensionality reduction that is widely used in machine learning and data science. By the end of the course, you will have a solid grasp of how PCA can be applied to streamline data and improve model performance.This course is designed to provide technical professionals with a thorough understanding of linear algebra's role in machine learning. By the end, you'll be well-equipped with the knowledge and skills needed to apply linear algebra in practical machine learning scenarios.

Data Engineering with AWS. Acquire the skills to design and build AWS-based data transformation pipelines like a pro - Second Edition

Gareth Eagar

This book, authored by a seasoned Senior Data Architect with 25 years of experience, aims to help you achieve proficiency in using the AWS ecosystem for data engineering. This revised edition provides updates in every chapter to cover the latest AWS services and features, takes a refreshed look at data governance, and includes a brand-new section on building modern data platforms which covers; implementing a data mesh approach, open-table formats (such as Apache Iceberg), and using DataOps for automation and observability.You'll begin by reviewing the key concepts and essential AWS tools in a data engineer's toolkit and getting acquainted with modern data management approaches. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how that transformed data is used by various data consumers. You’ll learn how to ensure strong data governance, and about populating data marts and data warehouses along with how a data lakehouse fits into the picture. After that, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. Then, you'll explore how the power of machine learning and artificial intelligence can be used to draw new insights from data. In the final chapters, you'll discover transactional data lakes, data meshes, and how to build a cutting-edge data platform on AWS.By the end of this AWS book, you'll be able to execute data engineering tasks and implement a data pipeline on AWS like a pro!

Rapid BeagleBoard Prototyping with MATLAB and Simulink. Leverage the power of Beagleboard to develop and deploy practical embedded projects

Fei Qin, Xuewu Dai

As an open source embedded single-board computer with many standard interfaces, Beagleboard is ideal for building embedded audio/video systems to realize your practical ideas. The challenge is how to design and implement a good digital processing algorithm on Beagleboard quickly and easily without intensive low-level coding.Rapid BeagleBoard Prototyping with MATLAB and Simulink is a practical, hands-on guide providing you with a number of clear, step-by-step exercises which will help you take advantage of the power of Beagleboard and give you a good grounding in rapid prototyping techniques for your audio/video applications.Rapid BeagleBoard Prototyping with MATLAB and Simulink looks at rapid prototyping and how to apply these techniques to your audio/video applications with Beagleboard quickly and painlessly without intensive manual low-level coding. It will take you through a number of clear, practical recipes that will help you to take advantage of both the Beagleboard hardware platform and Matlab/Simulink signal processing. We will also take a look at building S-function blocks that work as hardware drivers and interfaces for Matlab/Simulink. This gives you more freedom to explore the full range of advantages provided by Beagleboard.By the end of this book, you will have a clear idea about Beagleboard and Matlab/Simulink rapid prototyping as well as how to develop voice recognition systems, motion detection systems with I/O access, and serial communication for your own applications such as a smart home.

Data-Centric Machine Learning with Python. The ultimate guide to engineering and deploying high-quality models based on good data

Jonas Christensen, Nakul Bajaj, Manmohan Gosada, Kirk D. Borne

In the rapidly advancing data-driven world where data quality is pivotal to the success of machine learning and artificial intelligence projects, this critically timed guide provides a rare, end-to-end overview of data-centric machine learning (DCML), along with hands-on applications of technical and non-technical approaches to generating deeper and more accurate datasets.This book will help you understand what data-centric ML/AI is and how it can help you to realize the potential of ‘small data’. Delving into the building blocks of data-centric ML/AI, you’ll explore the human aspects of data labeling, tackle ambiguity in labeling, and understand the role of synthetic data. From strategies to improve data collection to techniques for refining and augmenting datasets, you’ll learn everything you need to elevate your data-centric practices. Through applied examples and insights for overcoming challenges, you’ll get a roadmap for implementing data-centric ML/AI in diverse applications in Python.By the end of this book, you’ll have developed a profound understanding of data-centric ML/AI and the proficiency to seamlessly integrate common data-centric approaches in the model development lifecycle to unlock the full potential of your machine learning projects by prioritizing data quality and reliability.