Data Engineering with Google Cloud Platform. A practical guide to operationalizing scalable data analytics systems on GCP

Adi Wijaya

With this book, you'll understand how the highly scalable Google Cloud Platform (GCP) enables data engineers to create end-to-end data pipelines right from storing and processing data and workflow orchestration to presenting data through visualization dashboards.Starting with a quick overview of the fundamental concepts of data engineering, you'll learn the various responsibilities of a data engineer and how GCP plays a vital role in fulfilling those responsibilities. As you progress through the chapters, you'll be able to leverage GCP products to build a sample data warehouse using Cloud Storage and BigQuery and a data lake using Dataproc. The book gradually takes you through operations such as data ingestion, data cleansing, transformation, and integrating data with other sources. You'll learn how to design IAM for data governance, deploy ML pipelines with the Vertex AI, leverage pre-built GCP models as a service, and visualize data with Google Data Studio to build compelling reports. Finally, you'll find tips on how to boost your career as a data engineer, take the Professional Data Engineer certification exam, and get ready to become an expert in data engineering with GCP.By the end of this data engineering book, you'll have developed the skills to perform core data engineering tasks and build efficient ETL data pipelines with GCP.

The Supervised Learning Workshop. Predict outcomes from data by building your own powerful predictive models with machine learning in Python - Second Edition

Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur

Would you like to understand how and why machine learning techniques and data analytics are spearheading enterprises globally? From analyzing bioinformatics to predicting climate change, machine learning plays an increasingly pivotal role in our society.Although the real-world applications may seem complex, this book simplifies supervised learning for beginners with a step-by-step interactive approach. Working with real-time datasets, you’ll learn how supervised learning, when used with Python, can produce efficient predictive models.Starting with the fundamentals of supervised learning, you’ll quickly move to understand how to automate manual tasks and the process of assessing date using Jupyter and Python libraries like pandas. Next, you’ll use data exploration and visualization techniques to develop powerful supervised learning models, before understanding how to distinguish variables and represent their relationships using scatter plots, heatmaps, and box plots. After using regression and classification models on real-time datasets to predict future outcomes, you’ll grasp advanced ensemble techniques such as boosting and random forests. Finally, you’ll learn the importance of model evaluation in supervised learning and study metrics to evaluate regression and classification tasks.By the end of this book, you’ll have the skills you need to work on your real-life supervised learning Python projects.

Building Data-Driven Applications with LlamaIndex. A practical guide to retrieval-augmented generation (RAG) to enhance LLM applications

Discover the immense potential of Generative AI and Large Language Models (LLMs) with this comprehensive guide. Learn to overcome LLM limitations, such as contextual memory constraints, prompt size issues, real-time data gaps, and occasional ‘hallucinations’. Follow practical examples to personalize and launch your LlamaIndex projects, mastering skills in ingesting, indexing, querying, and connecting dynamic knowledge bases. From fundamental LLM concepts to LlamaIndex deployment and customization, this book provides a holistic grasp of LlamaIndex's capabilities and applications. By the end, you'll be able to resolve LLM challenges and build interactive AI-driven applications using best practices in prompt engineering and troubleshooting Generative AI projects.

Practical Machine Learning with R. Define, build, and evaluate machine learning models for real-world applications

Brindha Priyadarshini Jeyaraman, Ludvig Renbo Olsen, Monicah Wambugu

With huge amounts of data being generated every moment, businesses need applications that apply complex mathematical calculations to data repeatedly and at speed. With machine learning techniques and R, you can easily develop these kinds of applications in an efficient way.Practical Machine Learning with R begins by helping you grasp the basics of machine learning methods, while also highlighting how and why they work. You will understand how to get these algorithms to work in practice, rather than focusing on mathematical derivations. As you progress from one chapter to another, you will gain hands-on experience of building a machine learning solution in R. Next, using R packages such as rpart, random forest, and multiple imputation by chained equations (MICE), you will learn to implement algorithms including neural net classifier, decision trees, and linear and non-linear regression. As you progress through the book, you’ll delve into various machine learning techniques for both supervised and unsupervised learning approaches. In addition to this, you’ll gain insights into partitioning the datasets and mechanisms to evaluate the results from each model and be able to compare them. By the end of this book, you will have gained expertise in solving your business problems, starting by forming a good problem statement, selecting the most appropriate model to solve your problem, and then ensuring that you do not overtrain it.

Mastering Kotlin for Android 14. Build powerful Android apps from scratch using Jetpack libraries and Jetpack Compose

Written with the best practices, this book will help you master Kotlin and use its powerful language features, libraries, tools, and APIs to elevate your Android apps.As you progress, you'll use Jetpack Compose and Material Design 3 to build UIs for your app, explore how to architect and improve your app architecture, and use Jetpack Libraries like Room and DataStore to persist your data locally. Using a step-by-step approach, this book will teach you how to debug issues in your app, detect leaks, inspect network calls fired by your app, and inspect your Room database. You'll also add tests to your apps to detect and address code smells.Toward the end, you’ll learn how to publish apps to the Google Play Store and see how to automate the process of deploying consecutive releases using GitHub actions, as well as learn how to distribute test builds to Firebase App Distribution. Additionally, the book covers tips on how to increase user engagement.By the end of this Kotlin book, you’ll be able to develop market-ready apps, add tests to their codebase, address issues, and get them in front of the right audience.

JIRA 7 Administration Cookbook. Over 80 hands-on recipes to help you efficiently administer, customize, and extend your JIRA 7 implementation - Second Edition

Patrick Li

JIRA 7 Administration Cookbook, Second Edition covers all the new major features that provide better prioritizingcapabilities, enhanced visibility, and the ability to customize JIRA application to meet your needs. We startby upgrading your existing JIRA instance and working through tasks you can perform at the server level to bettermaintain it. We then delve deep into adapting JIRA to your organization's needs, starting with the visual elements of setting up custom forms to capturing important data with custom fields and screens, and moving on to ensuringdata integrity through defining field behaviors. You'll gain insights into JIRA's e-mail capabilities, including managing outgoing e-mail rules and processing incoming e-mails for automated issue creation. The book contains tips and tricks that will make things easier for you as administrators, such as running scripts to automate tasks, getting easy access to logs, and working with tools to troubleshoot problems. The book concludes with a chapter on JIRA Service Desk, which will enable you to set up and customize your own support portal, work with internal teams to solve problems, and achieve optimized services with SLA.

C++ Windows Programming. Click here to enter text

Stefan Björnander

It is critical that modern developers have the right tools to build practical, user-friendly, and efficient applications in order to compete in today’s market. Through hands-on guidance, this book illustrates and demonstrates C++ best practices and the Small Windows object-oriented class library to ease your development of interactive Windows applications. Begin with a focus on high level application development using Small Windows. Learn how to build four real-world applications which focus on the general problems faced when developing graphical applications. Get essential troubleshooting guidance on drawing, spreadsheet, and word processing applications. Finally finish up with a deep dive into the workings of the Small Windows class library, which will give you all the insights you need to build your own object-oriented class library in C++.

Machine Learning Techniques for Text. Apply modern techniques with Python for text processing, dimensionality reduction, classification, and evaluation

Nikos Tsourakis

With the ever-increasing demand for machine learning and programming professionals, it's prime time to invest in the field. This book will help you in this endeavor, focusing specifically on text data and human language by steering a middle path among the various textbooks that present complicated theoretical concepts or focus disproportionately on Python code.A good metaphor this work builds upon is the relationship between an experienced craftsperson and their trainee. Based on the current problem, the former picks a tool from the toolbox, explains its utility, and puts it into action. This approach will help you to identify at least one practical use for each method or technique presented. The content unfolds in ten chapters, each discussing one specific case study. For this reason, the book is solution-oriented. It's accompanied by Python code in the form of Jupyter notebooks to help you obtain hands-on experience. A recurring pattern in the chapters of this book is helping you get some intuition on the data and then implement and contrast various solutions.By the end of this book, you'll be able to understand and apply various techniques with Python for text preprocessing, text representation, dimensionality reduction, machine learning, language modeling, visualization, and evaluation.