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!

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.

XGBoost for Regression Predictive Modeling and Time Series Analysis. Learn how to build, evaluate, and deploy predictive models with expert guidance

Partha Pritam Deka, Joyce Weiner, Prof. Roberto V. Zicari

XGBoost offers a powerful solution for regression and time series analysis, enabling you to build accurate and efficient predictive models. In this book, the authors draw on their combined experience of 40+ years in the semiconductor industry to help you harness the full potential of XGBoost, from understanding its core concepts to implementing real-world applications.As you progress, you'll get to grips with the XGBoost algorithm, including its mathematical underpinnings and its advantages over other ensemble methods. You'll learn when to choose XGBoost over other predictive modeling techniques, and get hands-on guidance on implementing XGBoost using both the Python API and scikit-learn API. You'll also get to grips with essential techniques for time series data, including feature engineering, handling lag features, encoding techniques, and evaluating model performance. A unique aspect of this book is the chapter on model interpretability, where you'll use tools such as SHAP, LIME, ELI5, and Partial Dependence Plots (PDP) to understand your XGBoost models. Throughout the book, you’ll work through several hands-on exercises and real-world datasets.By the end of this book, you'll not only be building accurate models but will also be able to deploy and maintain them effectively, ensuring your solutions deliver real-world impact.

Mastering Customer Success. Discover tactics to decrease churn and expand revenue

Jeff Mar, Peter Armaly, Mark Stouse

The rapidly evolving customer success landscape has left many Customer Success Managers (CSMs) struggling to keep pace with the complexities of this dynamic role. This Customer Success book bridges the gap by providing a comprehensive framework for mastering the essential skills required to excel.You’ll find out why the Customer Success function is indispensable today and gain expert insights into account segmentation, financial tiering, ideal customer profiles, and the complexity of customer engagement process design. You’ll then delve into playbook development, where you’ll find practical guidance for CSMs and Customer Success Operations Managers who want to improve their ability to drive desired business outcomes. Through insightful case studies, the authors illustrate their own experience of successful Customer Success implementation, showing you what it takes to exceed customer expectations with well-designed, proactive services. The journey doesn’t end there—it extends to highlighting the resilience required to build and operate successful Customer Success organizations.By the end of this guide, you’ll be equipped with the tactics and mindset necessary to stand out as a world-class Customer Success leader in your organization, driving growth at every turn.

Modern CMake for C++. Effortlessly build cutting-edge C++ code and deliver high-quality solutions - Second Edition

Rafał Świdziński, Alexander Kushnir

Modern CMake for C++ isn't just another reference book, or a repackaging of the documentation, but a blueprint to bridging the gap between learning C++ and being able to use it in a professional setting. It's an end-to-end guide to the automation of complex tasks, including building, testing, and packaging software.This second edition is significantly rewritten, restructured and refreshed with latest additions to CMake, such as support of C++20 Modules.In this book, you'll not only learn how to use the CMake language in CMake projects but also discover how to make those projects maintainable, elegant, and clean. As you progress, you'll dive into the structure of source directories, building targets, and packages, all while learning how to compile and link executables and libraries. You'll also gain a deeper understanding of how those processes work and how to optimize builds in CMake for the best results. You'll discover how to use external dependencies in your project – third-party libraries, testing frameworks, program analysis tools, and documentation generators. Finally, you'll gain profi ciency in exporting, installing, and packaging for internal and external purposes.By the end of this book, you'll be able to use CMake confi dently at a professional level.