Pyspark Essentials

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Pyspark Essentials

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Author : Robert Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-01-08

Pyspark Essentials written by Robert Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-08 with Computers categories.

"PySpark Essentials: A Practical Guide to Distributed Computing" is an expertly crafted resource designed to demystify the complexities of distributed data processing with PySpark. Offering an in-depth exploration of PySpark's integration within the Apache Spark ecosystem, this book serves as a foundational text for both newcomers and seasoned data professionals. Readers will gain comprehensive insights into setting up their PySpark environment, navigating its core architecture, and harnessing its power for efficient data manipulation and analysis. Structured to enhance practical understanding, this guide covers a wide array of topics, from the creation and management of DataFrames and Datasets to advanced data processing with Resilient Distributed Datasets (RDDs). It delves into PySpark SQL, empowering users with the ability to perform sophisticated data queries, and explores MLlib for large-scale machine learning applications. The book also highlights strategies for optimizing PySpark applications and managing real-time data with PySpark Streaming. Through clearly defined best practices and troubleshooting tips, readers will be equipped to overcome common challenges, ensuring they can build robust, scalable, and effective data processing solutions. Whether aiming to enter the field of big data or to enhance current skills, this book offers the essential toolkit for mastering PySpark.

Essential Pyspark For Scalable Data Analytics

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Author : Sreeram Nudurupati
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-10-29

Essential Pyspark For Scalable Data Analytics written by Sreeram Nudurupati and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-29 with Computers categories.

Get started with distributed computing using PySpark, a single unified framework to solve end-to-end data analytics at scale Key FeaturesDiscover how to convert huge amounts of raw data into meaningful and actionable insightsUse Spark's unified analytics engine for end-to-end analytics, from data preparation to predictive analyticsPerform data ingestion, cleansing, and integration for ML, data analytics, and data visualizationBook Description Apache Spark is a unified data analytics engine designed to process huge volumes of data quickly and efficiently. PySpark is Apache Spark's Python language API, which offers Python developers an easy-to-use scalable data analytics framework. Essential PySpark for Scalable Data Analytics starts by exploring the distributed computing paradigm and provides a high-level overview of Apache Spark. You'll begin your analytics journey with the data engineering process, learning how to perform data ingestion, cleansing, and integration at scale. This book helps you build real-time analytics pipelines that help you gain insights faster. You'll then discover methods for building cloud-based data lakes, and explore Delta Lake, which brings reliability to data lakes. The book also covers Data Lakehouse, an emerging paradigm, which combines the structure and performance of a data warehouse with the scalability of cloud-based data lakes. Later, you'll perform scalable data science and machine learning tasks using PySpark, such as data preparation, feature engineering, and model training and productionization. Finally, you'll learn ways to scale out standard Python ML libraries along with a new pandas API on top of PySpark called Koalas. By the end of this PySpark book, you'll be able to harness the power of PySpark to solve business problems. What you will learnUnderstand the role of distributed computing in the world of big dataGain an appreciation for Apache Spark as the de facto go-to for big data processingScale out your data analytics process using Apache SparkBuild data pipelines using data lakes, and perform data visualization with PySpark and Spark SQLLeverage the cloud to build truly scalable and real-time data analytics applicationsExplore the applications of data science and scalable machine learning with PySparkIntegrate your clean and curated data with BI and SQL analysis toolsWho this book is for This book is for practicing data engineers, data scientists, data analysts, and data enthusiasts who are already using data analytics to explore distributed and scalable data analytics. Basic to intermediate knowledge of the disciplines of data engineering, data science, and SQL analytics is expected. General proficiency in using any programming language, especially Python, and working knowledge of performing data analytics using frameworks such as pandas and SQL will help you to get the most out of this book.

Machine Learning In Python

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Author : Michael Bowles
language : en
Publisher: John Wiley & Sons
Release Date : 2015-04-27

Machine Learning In Python written by Michael Bowles and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-27 with Computers categories.

Learn a simpler and more effective way to analyze data and predict outcomes with Python Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this book is able to provide full descriptions of the mechanisms at work, and the examples that illustrate the machinery with specific, hackable code. The algorithms are explained in simple terms with no complex math and applied using Python, with guidance on algorithm selection, data preparation, and using the trained models in practice. You will learn a core set of Python programming techniques, various methods of building predictive models, and how to measure the performance of each model to ensure that the right one is used. The chapters on penalized linear regression and ensemble methods dive deep into each of the algorithms, and you can use the sample code in the book to develop your own data analysis solutions. Machine learning algorithms are at the core of data analytics and visualization. In the past, these methods required a deep background in math and statistics, often in combination with the specialized R programming language. This book demonstrates how machine learning can be implemented using the more widely used and accessible Python programming language. Predict outcomes using linear and ensemble algorithm families Build predictive models that solve a range of simple and complex problems Apply core machine learning algorithms using Python Use sample code directly to build custom solutions Machine learning doesn't have to be complex and highly specialized. Python makes this technology more accessible to a much wider audience, using methods that are simpler, effective, and well tested. Machine Learning in Python shows you how to do this, without requiring an extensive background in math or statistics.

Python Data Science Essentials

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Author : MARK JOHN LADO
language : en
Publisher: Amazon Digital Services LLC - Kdp
Release Date : 2024-03-18

Python Data Science Essentials written by MARK JOHN LADO and has been published by Amazon Digital Services LLC - Kdp this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-18 with Computers categories.

The field of data science has emerged as a critical component in extracting actionable insights and making informed decisions from vast amounts of data. This comprehensive guide explores the fundamentals of data science using the Python language, a versatile toolset widely adopted in the industry. The journey begins with an introduction to data science, outlining its principles, methodologies, and real-world applications. Next, the basics of Python programming are covered, providing a solid foundation for data manipulation and analysis. Data types and structures in Python are then explored, followed by an in-depth look at essential libraries such as NumPy and Pandas, which facilitate efficient data handling and manipulation. The importance of data visualization is emphasized through tutorials on Matplotlib and Seaborn, enabling effective communication of insights and trends. Data cleaning and preprocessing techniques are discussed, addressing common challenges in data quality and preparation. Statistical analysis is introduced as a fundamental aspect of data science, showcasing its applications in hypothesis testing, correlation analysis, and regression modeling using Python. Machine learning concepts are then explored, covering both supervised and unsupervised learning algorithms, including linear regression, decision trees, clustering, and dimensionality reduction. Model evaluation and validation techniques are essential for assessing model performance and generalization ability, ensuring robust and reliable predictions. Additionally, an introduction to deep learning with Python provides insights into advanced neural network architectures and their applications in solving complex problems. Handling big data is a critical aspect of modern data science, and this guide provides an overview of using Python and Spark for scalable and distributed data processing. Real-world case studies across various domains illustrate the practical applications of data science techniques, from e-commerce recommendation systems to healthcare analytics. Finally, best practices and tips for data science projects are discussed, highlighting key considerations for project success, including data exploration, feature engineering, model selection, and collaboration. By mastering these fundamentals, aspiring data scientists can embark on their journey with confidence, equipped to tackle real-world challenges and drive impactful insights from data.

Python Data Science Essentials

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Author : Alberto Boschetti
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-09-28

Python Data Science Essentials written by Alberto Boschetti and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-28 with Computers categories.

Gain useful insights from your data using popular data science tools Key FeaturesA one-stop guide to Python libraries such as pandas and NumPyComprehensive coverage of data science operations such as data cleaning and data manipulationChoose scalable learning algorithms for your data science tasksBook Description Fully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn. The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost. By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users What you will learnSet up your data science toolbox on Windows, Mac, and LinuxUse the core machine learning methods offered by the scikit-learn libraryManipulate, fix, and explore data to solve data science problemsLearn advanced explorative and manipulative techniques to solve data operationsOptimize your machine learning models for optimized performanceExplore and cluster graphs, taking advantage of interconnections and links in your dataWho this book is for If you’re a data science entrant, data analyst, or data engineer, this book will help you get ready to tackle real-world data science problems without wasting any time. Basic knowledge of probability/statistics and Python coding experience will assist you in understanding the concepts covered in this book.

Databricks Essentials

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Author : Robert Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-01-06

Databricks Essentials written by Robert Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-06 with Computers categories.

"Databricks Essentials: A Guide to Unified Data Analytics" delivers a comprehensive exploration of the contemporary Databricks platform, designed to empower professionals seeking to harness the capabilities of data analytics, engineering, and machine learning in an integrated environment. This book provides a structured approach, guiding readers through meticulously crafted chapters that cover every aspect of Databricks—from establishing a foundational understanding to advanced performance optimization and security best practices. Each chapter is developed with accessibility and practical application in mind, ensuring that both beginners and seasoned data professionals can benefit from its insights. As organizations face increasing demands for data-driven decision-making, the need for a unified analytics platform has never been more critical. This book unravels the intricacies of Databricks, showcasing its potential to streamline workflows and revolutionize data operations through collaborative tools and real-time processing capabilities. Readers will discover how to optimize resources, implement scalable solutions, and leverage machine learning to drive results. Enhanced by illustrative case studies and practical examples, "Databricks Essentials" not only educates but also inspires readers to explore new frontiers in data analytics, making it an indispensable resource for those committed to innovation and excellence in the field.

Spark The Definitive Guide

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Author : Bill Chambers
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2018-02-08

Spark The Definitive Guide written by Bill Chambers and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-08 with Computers categories.

Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. Youâ??ll explore the basic operations and common functions of Sparkâ??s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Sparkâ??s scalable machine-learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasetsâ??Sparkâ??s core APIsâ??through worked examples Dive into Sparkâ??s low-level APIs, RDDs, and execution of SQL and DataFrames Understand how Spark runs on a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Structured Streaming, Sparkâ??s stream-processing engine Learn how you can apply MLlib to a variety of problems, including classification or recommendation

Apache Sedona Essentials

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Author : Robert Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-01-06

Apache Sedona Essentials written by Robert Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-06 with Computers categories.

"Apache Sedona Essentials: A Practical Guide to Spatial Data Processing" is meticulously crafted for beginners and professionals alike, offering a comprehensive overview of Apache Sedona's capabilities and applications in handling spatial data. This book serves as a definitive resource, equipping readers with the foundation needed to manage, query, and analyze spatial datasets efficiently using Sedona. Each chapter is structured to guide you progressively through core concepts and advanced techniques, ensuring a robust understanding of the functionalities that Apache Sedona provides. Focused on real-world applicability, this guide explores Sedona's integration within big data ecosystems, its performance optimization strategies, and the implementation of advanced spatial processing methods. From setting up your development environment to exploring complex spatial operations and deriving insights from data analytics, this book prepares you to tackle a variety of spatial data challenges across diverse domains. Through practical examples, detailed explanations, and best practice recommendations, readers will gain the skills needed to harness the full potential of spatial data intelligence using Apache Sedona.

Python Machine Learning By Example

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Author : Yuxi (Hayden) Liu
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-10-30

Python Machine Learning By Example written by Yuxi (Hayden) Liu and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-30 with Computers categories.

A comprehensive guide to get you up to speed with the latest developments of practical machine learning with Python and upgrade your understanding of machine learning (ML) algorithms and techniques Key FeaturesDive into machine learning algorithms to solve the complex challenges faced by data scientists todayExplore cutting edge content reflecting deep learning and reinforcement learning developmentsUse updated Python libraries such as TensorFlow, PyTorch, and scikit-learn to track machine learning projects end-to-endBook Description Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML). With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements. At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries. Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP. By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems. What you will learnUnderstand the important concepts in ML and data scienceUse Python to explore the world of data mining and analyticsScale up model training using varied data complexities with Apache SparkDelve deep into text analysis and NLP using Python libraries such NLTK and GensimSelect and build an ML model and evaluate and optimize its performanceImplement ML algorithms from scratch in Python, TensorFlow 2, PyTorch, and scikit-learnWho this book is for If you’re a machine learning enthusiast, data analyst, or data engineer highly passionate about machine learning and want to begin working on machine learning assignments, this book is for you. Prior knowledge of Python coding is assumed and basic familiarity with statistical concepts will be beneficial, although this is not necessary.

Essential Guide To Llmops

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Author : RYAN. DOAN
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-07-31

Essential Guide To Llmops written by RYAN. DOAN and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-31 with Computers categories.

Unlock the secrets to mastering LLMOps with innovative approaches to streamline AI workflows, improve model efficiency, and ensure robust scalability, revolutionizing your language model operations from start to finish Key Features Gain a comprehensive understanding of LLMOps, from data handling to model governance Leverage tools for efficient LLM lifecycle management, from development to maintenance Discover real-world examples of industry cutting-edge trends in generative AI operation Purchase of the print or Kindle book includes a free PDF eBook Book Description The rapid advancements in large language models (LLMs) bring significant challenges in deployment, maintenance, and scalability. This Essential Guide to LLMOps provides practical solutions and strategies to overcome these challenges, ensuring seamless integration and the optimization of LLMs in real-world applications. This book takes you through the historical background, core concepts, and essential tools for data analysis, model development, deployment, maintenance, and governance. You’ll learn how to streamline workflows, enhance efficiency in LLMOps processes, employ LLMOps tools for precise model fine-tuning, and address the critical aspects of model review and governance. You’ll also get to grips with the practices and performance considerations that are necessary for the responsible development and deployment of LLMs. The book equips you with insights into model inference, scalability, and continuous improvement, and shows you how to implement these in real-world applications. By the end of this book, you’ll have learned the nuances of LLMOps, including effective deployment strategies, scalability solutions, and continuous improvement techniques, equipping you to stay ahead in the dynamic world of AI. What you will learn Understand the evolution and impact of LLMs in AI Differentiate between LLMOps and traditional MLOps Utilize LLMOps tools for data analysis, preparation, and fine-tuning Master strategies for model development, deployment, and improvement Implement techniques for model inference, serving, and scalability Integrate human-in-the-loop strategies for refining LLM outputs Grasp the forefront of emerging technologies and practices in LLMOps Who this book is for This book is for machine learning professionals, data scientists, ML engineers, and AI leaders interested in LLMOps. It is particularly valuable for those developing, deploying, and managing LLMs, as well as academics and students looking to deepen their understanding of the latest AI and machine learning trends. Professionals in tech companies and research institutions, as well as anyone with foundational knowledge of machine learning will find this resource invaluable for advancing their skills in LLMOps.