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Free Download Data Labeling in Machine Learning with Python: Explore modern ways to prepare labeled data for training and fine-tuning ML and generative AI models by Vijaya Kumar Suda English | January 31, 2024 | ISBN: 1804610542 | 398 pages | PDF | 37 Mb Take your data preparation, machine learning, and GenAI skills to the next level by learning a range of Python algorithms and tools for data labelingKey FeaturesGenerate labels for regression in scenarios with limited training dataApply generative AI and large language models (LLMs) to explore and label text dataLeverage Python libraries for image, video, and audio data analysis and data labelingPurchase of the print or Kindle book includes a free PDF eBookBook Description Data labeling is the invisible hand that guides the power of artificial intelligence and machine learning. In today's data-driven world, mastering data labeling is not just an advantage, it's a necessity. Data Labeling in Machine Learning with Python empowers you to unearth value from raw data, create intelligent systems, and influence the course of technological evolution. With this book, you'll discover the art of employing summary statistics, weak supervision, programmatic rules, and heuristics to assign labels to unlabeled training data programmatically. As you progress, you'll be able to enhance your datasets by mastering the intricacies of semi-supervised learning and data augmentation. Venturing further into the data landscape, you'll immerse yourself in the annotation of image, video, and audio data, harnessing the power of Python libraries such as seaborn, matDescriptionlib, cv2, librosa, openai, and langchain. With hands-on guidance and practical examples, you'll gain proficiency in annotating diverse data types effectively. By the end of this book, you'll have the practical expertise to programmatically label diverse data types and enhance datasets, unlocking the full potential of your data.What you will learnExcel in exploratory data analysis (EDA) for tabular, text, audio, video, and image dataUnderstand how to use Python libraries to apply rules to label raw dataDiscover data augmentation techniques for adding classification labelsLeverage K-means clustering to classify unsupervised dataExplore how hybrid supervised learning is applied to add labels for classificationMaster text data classification with generative AIDetect objects and classify images with OpenCV and YOLOUncover a range of techniques and resources for data annotationWho this book is for This book is for machine learning engineers, data scientists, and data engineers who want to learn data labeling methods and algorithms for model training. Data enthusiasts and Python developers will be able to use this book to learn data exploration and annotation using Python libraries. Basic Python knowledge is beneficial but not necessary to get started.Table of ContentsExploring Data for Machine LearningLabeling Data for ClassificationLabeling Data for RegressionExploring Image DataLabeling Image Data Using RulesLabeling Image Data Using Data AugmentationLabeling Text DataExploring Video DataLabeling Video DataExploring Audio DataLabeling Audio DataHands-On Exploring Data Labeling Tools Rapidgator https://rg.to/file/fbfd536495a5df45d04de01c27c1c52b/nviyh.rar.html TakeFile https://takefile.link/bfy84hn19nuw/nviyh.rar.html Links are Interchangeable - Single Extraction

Free Download Michael Genesereth, "Data Integration: The Relational Logic Approach " English | ISBN: 3031004221 | 2010 | 112 pages | PDF | 2 MB Data integration is a critical problem in our increasingly interconnected but inevitably heterogeneous world. There are numerous data sources available in organizational databases and on public information systems like the World Wide Web. Not surprisingly, the sources often use different vocabularies and different data structures, being created, as they are, by different people, at different times, for different purposes. The goal of data integration is to provide programmatic and human users with integrated access to multiple, heterogeneous data sources, giving each user the illusion of a single, homogeneous database designed for his or her specific need. The good news is that, in many cases, the data integration process can be automated. This book is an introduction to the problem of data integration and a rigorous account of one of the leading approaches to solving this problem, viz., the relational logic approach. Relational logic provides a theoretical framework for discussing data integration. Moreover, in many important cases, it provides algorithms for solving the problem in a computationally practical way. In many respects, relational logic does for data integration what relational algebra did for database theory several decades ago. A companion web site provides interactive demonstrations of the algorithms. Table of Contents: Preface / Interactive Edition / Introduction / Basic Concepts / Query Folding / Query Planning / Master Schema Management / Appendix / References / Index / Author Biography Don't have access? Recommend our Synthesis Digital Library to your library or purchase a personal subscription. Email [email protected] for details. Read more Rapidgator https://rg.to/file/6d241f5a7ee2185519c55d0f4f8cee27/g2gkf.rar.html TakeFile https://takefile.link/8qshrlti4i1r/g2gkf.rar.html Links are Interchangeable - Single Extraction

Free Download Data Grab: The New Colonialism of Big Tech and How to Fight Back by Ulises A. Mejias, Nick Couldry English | March 14th, 2024 | ISBN: 0226832309 | 224 pages | True EPUB | 1.17 MB A compelling argument that the extractive practices of today's tech giants are the continuation of colonialism-and a crucial guide to collective resistance. Large technology companies like Meta, Amazon, and Alphabet have unprecedented access to our daily lives, collecting information when we check our email, count our steps, shop online, and commute to and from work. Current events are concerning-both the changing owners (and names) of billion-dollar tech companies and regulatory concerns about artificial intelligence underscore the sweeping nature of Big Tech's surveillance and the influence such companies hold over the people who use their apps and platforms. As trusted tech experts Ulises A. Mejias and Nick Couldry show in this eye-opening and convincing book, this vast accumulation of data is not the accidental stockpile of a fast-growing industry. Just as nations stole territories for ill-gotten minerals and crops, wealth, and dominance, tech companies steal personal data important to our lives. It's only within the framework of colonialism, Mejias and Couldry argue, that we can comprehend the full scope of this heist. Like the land grabs of the past, today's data grab converts our data into raw material for the generation of corporate profit against our own interests. Like historical colonialism, today's tech corporations have engineered an extractive form of doing business that builds a new social and economic order, leads to job precarity, and degrades the environment. These methods deepen global inequality, consolidating corporate wealth in the Global North and engineering discriminatory algorithms. Promising convenience, connection, and scientific progress, tech companies enrich themselves by encouraging us to relinquish details about our personal interactions, our taste in movies or music, and even our health and medical records. Do we have any other choice? Data Grab affirms that we do. To defy this new form of colonialism we will need to learn from previous forms of resistance and work together to imagine entirely new ones. Mejias and Couldry share the stories of voters, workers, activists, and marginalized communities who have successfully opposed unscrupulous tech practices. An incisive discussion of the digital media that's transformed our world, Data Grab is a must-read for anyone concerned about privacy, self-determination, and justice in the internet age. Rapidgator https://rg.to/file/a08722cc7b404ca9b554a3e243cbd498/qybdt.rar.html TakeFile https://takefile.link/wt800oqbw18t/qybdt.rar.html Links are Interchangeable - Single Extraction

Free Download Data Engineering with Scala and Spark: A practical guide helping you build streaming and batch pipelines that process massive amounts of data using Scala by Eric Tome, David Radford, Rupam Bhattacharjee English | February 9, 2024 | ISBN: 1804612588 | 323 pages | EPUB | 7.96 Mb Take your data engineering skills to the next level by learning to utilize Scala and functional programming to create continuous and scheduled pipelines that ingest, transform, and aggregate data Key FeaturesTransform data into a clean and trusted source of information for your organization using ScalaBuild streaming and batch-processing pipelines with step-by-step explanationsImplement and orchestrate your pipelines by following CI/CD best practices and test-driven developmentBook Description Performance in distributed computing environments is a critical factor for data engineers. If performance in a distributed computing environment is not optimal, several consequences can arise like slow data processing, bottlenecks and latency, inefficient resource utilization, etc. impacting the overall efficiency and effectiveness of data engineering tasks. While Python remains a popular choice for data engineering due to its ease of use, Scala shines in scenarios where the performance of distributed data processing is paramount. This book will teach you to leverage the Scala programming language on the Spark framework and the latest cloud technologies to build continuous and triggered data pipelines. You will do this by setting up a data engineering environment for local development and scalable distributed cloud deployments using data engineering best practices, test-driven development, and CI/CD. You will then understand DataFrame API, Dataset API, and Spark SQL API and its use. You will further learn about data profiling and quality in Scala. You will also orchestrate and performance-tune your end-to-end pipelines to deliver data to your end users. By the end of this book, you will be able to build streaming and batch data pipelines using Scala while following software engineering best practices. What you will learnSet up your development environment to build pipelines in ScalaUse polymorphic functions, type parameterization, and scala implicitUse Spark DataFrames, Datasets, and Spark SQL with ScalaRead and write data to object storesBuild and chain data transforms using ScalaProfile and clean your data using DeequPerformance-tune your data pipelines using ScalaWho This Book Is For This book is aimed at data engineers who are experienced in working with data but want to understand how to transform raw data into a clean, trusted, and valuable source of information for their organization using Scala and the latest cloud technologies. Table of ContentsScala Essentials for Data Engineers - A Quick TourEnvironment Setup - Install IDE, Spark, MySQL and Object StorageIntroduction to Spark and its APIs - Dataframe, Dataset and SQLData Ingestion and Targets - DatabasesData Ingestion and Targets - Object Stores, Streaming Sources and SinksData Transformation - Selection, Filtering, Sorting, Aggregation, Joins, and Working with Complex TypesData Profiling and Data Quality - DeequTest Driven Development, Code Health and MaintainabilityCI/CD with GitHubOrchestrating Your Data Engineering PipelinesPerformance TuningBuilding Batch Pipelines using Spark & ScalaBuilding Streaming Pipelines using Spark & Scala Rapidgator https://rg.to/file/3c2034a3ec9a296c5c80251ecdfab575/4ilei.rar.html TakeFile https://takefile.link/78p73i67ye9b/4ilei.rar.html Links are Interchangeable - Single Extraction

Free Download Data Engineering with Google Cloud Platform: A guide to leveling up as a data engineer by building a scalable data platform with Google Cloud by Adi Wijaya English | April 30, 2024 | ISBN: 1835080111 | 478 pages | PDF | 45 Mb Become a successful data engineer by building and deploying your own data pipelines on Google Cloud, including making key architectural decisions Key FeaturesGet up to speed with data governance on Google CloudLearn how to use various Google Cloud products like Dataform, DLP, Dataplex, Dataproc Serverless, and DatastreamBoost your confidence by getting Google Cloud data engineering certification guidance from real exam experiencesBook Description The second edition of Data Engineering with Google Cloud builds upon the success of the first edition by offering enhanced clarity and depth to data professionals navigating the intricate landscape of data engineering. Beyond its foundational lessons, this new edition delves into the essential realm of data governance within Google Cloud, providing you invaluable insights into managing and optimizing data resources effectively. Furthermore, this book helps you stay ahead of the curve by guiding you through the latest technological advancements in the Google Cloud ecosystem. You'll cover essential aspects, from exploring Cloud Composer 2 to the evolution of Airflow 2.5. Additionally, you'll explore how to work with cutting-edge tools like Dataform, DLP, Dataplex, Dataproc Serverless, and Datastream to perform data governance on datasets. By the end of this book, you'll be equipped to navigate the ever-evolving world of data engineering on Google Cloud, from foundational principles to cutting-edge practices. What you will learnLoad data into BigQuery and materialize its outputFocus on data pipeline orchestration using Cloud ComposerFormulate Airflow jobs to orchestrate and automate a data warehouseEstablish a Hadoop data lake, generate ephemeral clusters, and execute jobs on the Dataproc clusterHarness Pub/Sub for messaging and ingestion for event-driven systemsApply Dataflow to conduct ETL on streaming dataImplement data governance services on Google CloudWho this book is for Data analysts, IT practitioners, software engineers, or any data enthusiasts looking to have a successful data engineering career will find this book invaluable. Additionally, experienced data professionals who want to start using Google Cloud to build data platforms will get clear insights on how to navigate the path. Whether you're a beginner who wants to explore the fundamentals or a seasoned professional seeking to learn the latest data engineering concepts, this book is for you. Table of ContentsFundamentals of Data engineering with GCPBig Data Capabilities on GCPBuilding a data warehouse in BigQueryBuild Orchestration for Batch Data Loading Using Cloud ComposerBuilding a Data Lake using DataprocProcess Streaming Data with Datastream, Pub/Sub and DataflowVisualizing Data for Making Data-Driven Decisions with Looker StudioBuild machine learning solutions on GCPUser and Project Management on GCPData Governance in GCPCost Strategy in GCPCI/CD on Google Cloud Platform for Data EngineersBoost your confidence as a Data Engineer Rapidgator https://rg.to/file/9ba80cba5d658c2a9fa0db130c85be53/ty8f6.rar.html TakeFile https://takefile.link/sjidb1k3b5ne/ty8f6.rar.html Links are Interchangeable - Single Extraction

Free Download Data Engineering Design Patterns (Early Release) English | 2024 | ISBN: 9781098165826 | 300 pages | EPUB | 2.16 Mb Data projects are an intrinsic part of an organization's technical ecosystem, but data engineers in many companies are still trying to solve problems that others have already solved. This hands-on guide shows you how to provide valuable data by focusing on various aspects of data engineering, including data ingestion, data quality, idempotency, and more. Author Bartosz Konieczny guides you through the process of building reliable end-to-end data engineering projects, from data ingestion to data observability, focusing on data engineering design patterns that solve common business problems in a secure and storage-optimized manner. Each pattern includes a user-facing description of the problem, solutions, and consequences that place the pattern into the context of real-life scenarios. Throughout this journey, you'll use open source data tools and public cloud services to see how to put each pattern into practice. You learn Challenges data engineers face and their impact on data systems How these challenges relate to data system components What data engineering patterns are for How to identify and fix issues with your current data components Technology-agnostic solutions to new and existing data projects How to implement patterns with Apache Airflow, Apache Spark, Apache Flink, and Delta Lake Bartosz Konieczny is a freelance data engineer who's been coding for more than 15 years. He's held various senior hands-on positions that helped him work on many data engineering problems in batch and stream processing. Rapidgator https://rg.to/file/cbfd6b6b2f2e69acabca184a4e1af21b/e5js1.rar.html TakeFile https://takefile.link/bs8x6lab1jo0/e5js1.rar.html Links are Interchangeable - Single Extraction

Free Download Data Curious (Third Early Release) English | 2023 | ISBN: 9781098143824 | 194 pages | PDF | 4.20 Mb Citizen data analysts are emerging to fill in the gaps left by traditional and slow business intelligence processes. Business functions such as finance, supply chain, and sales are now leading new data/analytics initiatives rather than partnering with centralized IT or analytics teams. But there is a knowledge gap-these new "citizen data analysts" understand their area of business, but unlike their IT counterparts, they do not have a background in data processing technology. This book guides tech-savvy business professionals through the fundamentals of data literacy from understanding the possibilities to asking the right questions. It also offers practical advice on how to set up teams to be data self-sufficient and drive change. Rapidgator https://rg.to/file/5de2dc9164ce36d31b05ac593104d6ce/c94oj.rar.html TakeFile https://takefile.link/1yw3j5lvbcye/c94oj.rar.html Links are Interchangeable - Single Extraction

Free Download Data Analytics with Spark Using Python (Addison-Wesley Data & Analytics Series) by Jeffrey Aven English | June 6, 2018 | ISBN: 013484601X | True EPUB | 320 pages | 25.8 MB Solve Data Analytics Problems with Spark, PySpark, and Related Open Source Tools Spark is at the heart of today's Big Data revolution, helping data professionals supercharge efficiency and performance in a wide range of data processing and analytics tasks. In this guide, Big Data expert Jeffrey Aven covers all you need to know to leverage Spark, together with its extensions, subprojects, and wider ecosystem. Aven combines a language-agnostic introduction to foundational Spark concepts with extensive programming examples utilizing the popular and intuitive PySpark development environment. This guide's focus on Python makes it widely accessible to large audiences of data professionals, analysts, and developers―even those with little Hadoop or Spark experience. Aven's broad coverage ranges from basic to advanced Spark programming, and Spark SQL to machine learning. You'll learn how to efficiently manage all forms of data with Spark: streaming, structured, semi-structured, and unstructured. Throughout, concise topic overviews quickly get you up to speed, and extensive hands-on exercises prepare you to solve real problems. Coverage includes: * Understand Spark's evolving role in the Big Data and Hadoop ecosystems * Create Spark clusters using various deployment modes * Control and optimize the operation of Spark clusters and applications * Master Spark Core RDD API programming techniques * Extend, accelerate, and optimize Spark routines with advanced API platform constructs, including shared variables, RDD storage, and partitioning * Efficiently integrate Spark with both SQL and nonrelational data stores * Perform stream processing and messaging with Spark Streaming and Apache Kafka * Implement predictive modeling with SparkR and Spark MLlib Rapidgator https://rg.to/file/6f472cc6fdea27f7d3869a8ee1d384b2/30a55.rar.html TakeFile https://takefile.link/64gsa8t8u09e/30a55.rar.html Links are Interchangeable - Single Extraction

Free Download Data Analytics with Spark Using Python (Addison-Wesley Data & Analytics Series) by Jeffrey Aven English | June 6, 2018 | ISBN: 9780134846019 | 320 pages | MOBI | 31 Mb Solve Data Analytics Problems with Spark, PySpark, and Related Open Source Tools Spark is at the heart of today's Big Data revolution, helping data professionals supercharge efficiency and performance in a wide range of data processing and analytics tasks. In this guide, Big Data expert Jeffrey Aven covers all you need to know to leverage Spark, together with its extensions, subprojects, and wider ecosystem. Aven combines a language-agnostic introduction to foundational Spark concepts with extensive programming examples utilizing the popular and intuitive PySpark development environment. This guide's focus on Python makes it widely accessible to large audiences of data professionals, analysts, and developers―even those with little Hadoop or Spark experience. Aven's broad coverage ranges from basic to advanced Spark programming, and Spark SQL to machine learning. You'll learn how to efficiently manage all forms of data with Spark: streaming, structured, semi-structured, and unstructured. Throughout, concise topic overviews quickly get you up to speed, and extensive hands-on exercises prepare you to solve real problems. Coverage includes: * Understand Spark's evolving role in the Big Data and Hadoop ecosystems * Create Spark clusters using various deployment modes * Control and optimize the operation of Spark clusters and applications * Master Spark Core RDD API programming techniques * Extend, accelerate, and optimize Spark routines with advanced API platform constructs, including shared variables, RDD storage, and partitioning * Efficiently integrate Spark with both SQL and nonrelational data stores * Perform stream processing and messaging with Spark Streaming and Apache Kafka * Implement predictive modeling with SparkR and Spark MLlib Rapidgator https://rg.to/file/c96f90fbe1b3cd1587ed3069c007c24b/4nv1u.rar.html TakeFile https://takefile.link/w8nbmrjh5fmm/4nv1u.rar.html Links are Interchangeable - Single Extraction

Free Download Data Analytics for Marketing: A practical guide to analyzing marketing data using Python by Guilherme Diaz-Bérrio English | May 10, 2024 | ISBN: 1803241608 | 452 pages | PDF | 23 Mb Conduct data-driven marketing research and analysis with hands-on examples using Python by leveraging open-source tools and libraries Key FeaturesAnalyze marketing data using proper statistical techniquesUse data modeling and analytics to understand customer preferences and enhance strategies without complex mathImplement Python libraries like DoWhy, Pandas, and Prophet in a business setting with examples and use casesBook Description Most marketing professionals are familiar with various sources of customer data that promise insights for success. There are extensive sources of data, from customer surveys to digital marketing data. Moreover, there is an increasing variety of tools and techniques to shape data, from small to big data. However, having the right knowledge and understanding the context of how to use data and tools is crucial. In this book, you'll learn how to give context to your data and turn it into useful information. You'll understand how and where to use a tool or dataset for a specific question, exploring the "what and why questions" to provide real value to your stakeholders. Using Python, this book will delve into the basics of analytics and causal inference. Then, you'll focus on visualization and presentation, followed by understanding guidelines on how to present and condense large amounts of information into KPIs. After learning how to plan ahead and forecast, you'll delve into customer analytics and insights. Finally, you'll measure the effectiveness of your marketing efforts and derive insights for data-driven decision-making. By the end of this book, you'll understand the tools you need to use on specific datasets to provide context and shape your data, as well as to gain information to boost your marketing efforts.What you will learnUnderstand the basic ideas behind the main statistical models used in marketing analyticsApply the right models and tools to a specific analytical questionDiscover how to conduct causal inference, experimentation, and statistical modeling with PythonImplement common open source Python libraries for specific use cases with immediately applicable codeAnalyze customer lifetime data and generate customer insightsGo through the different stages of analytics, from descriptive to prescriptiveWho this book is for This book is for data analysts and data scientists working in a marketing team supporting analytics and marketing research, who want to provide better insights that lead to data-driven decision-making. Prior knowledge of Python, data analysis, and statistics is required to get the most out of this book. Table of ContentsWhat is Marketing Analytics?Extracting and Exploring Data with Singer and pandasDesign Principles and Presenting Results with StreamlitEconometrics and Causal Inference with Statsmodels and PyMCForecasting with Prophet, ARIMA, and Other Models Using StatsForecastAnomaly Detection with StatsForecast and PyMCCustomer Insights - Segmentation and RFMCustomer Lifetime Value with PyMC MarketingCustomer Survey AnalysisConjoint Analysis with pandas and StatsmodelsMulti-Touch Digital AttributionMedia Mix Modeling with PyMC MarketingRunning Experiments with PyMC Rapidgator https://rg.to/file/8da8182f8fa63cffaf1eec26447158e3/moy1g.rar.html TakeFile https://takefile.link/h6jgbvs5x7gs/moy1g.rar.html Links are Interchangeable - Single Extraction

Free Download Angelika Krebs, "Das Weltbild Der Igel: Naturethik Einmal Anders" Deutsch | 2021 | ISBN: 3796544142 | PDF | pages: 242 | 4.1 mb Wie wichtig Natur fur menschliches Gluck ist, als Ort der Freude am Leben oder zumindest der Zuflucht, ist zuletzt vielen von uns aufgegangen. Trotzdem hat Landschaft bisher keine Lobby. Dieses unkonventionelle Buch tritt fur die Schonheit der Natur ein. Es argumentiert gegen die Dominanz der anthropozentrisch-rechnenden Weltbemeisterung und fur eine asthetische Okozentrik. Unkonventionell ist das Buch aber auch deshalb, weil es die philosophische Arbeit an Begriff und Argument mit literarischen Passagen von grosser Leuchtkraft verbindet. Diese Passagen stammen aus Peter Kurzecks Roman Vorabend. Alle funf Kapitel: zum Leid der Igel, zur bedrohten Schonheit und Heiligkeit der Natur, zu ihrem Wert als Heimat und zu unserem besinnungslos zerstorerischen Konsum werden ausgehend von einer solchen Passage entfaltet. Denn es braucht die Literatur in der Naturethik. Rapidgator https://rg.to/file/d30ef7b28eb6252cd30404a362e1b9fb/u49yx.rar.html TakeFile https://takefile.link/9fi3jolusvgv/u49yx.rar.html Links are Interchangeable - Single Extraction

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Free Download Das Lied von der Erde: The Song of the Earth by Gustav Mahler English | February 22nd, 2018 | ISBN: 3795711274 | 161 pages | True PDF | 49.41 MB Over 100 works of the well-known Edition Eulenburg series of scores from orchestral and choral literature, chamber music and music theatre are now available in digital format. You can now enjoy the yellow study scores digitally with one click in excellent reproduction quality. Über 100 Werke der berühmten Edition Eulenburg Partiturreihe für Orchester- und Chorliteratur, Kammermusik und Musiktheater sind nun auch in einer digitalen Aufbereitung erhältlich. In optisch hervorragender Darstellung kann man die gelben Studienpartituren mit einem Klick jetzt auch digital genießen. Rapidgator https://rg.to/file/a870b16f11f1f9590757291d4ced7c55/vd5rm.rar.html TakeFile https://takefile.link/owqf2ayhs2mk/vd5rm.rar.html Links are Interchangeable - Single Extraction

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