Applied Machine Learning With Mllib

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Applied Machine Learning With Mllib

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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-03

Applied Machine Learning With Mllib written by Richard 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 2025-06-03 with Computers categories.

"Applied Machine Learning with MLlib" Harness the full potential of large-scale machine learning with "Applied Machine Learning with MLlib," a comprehensive guide designed for practitioners and engineers working in modern data environments. This book delves into the architectural pillars of Apache Spark and MLlib, illuminating the principles of distributed computing that enable robust, scalable machine learning solutions in production. Readers will gain a deep understanding of core internals, from resilient distributed datasets and resource management to API evolution and fault-tolerant deployment strategies—empowering them to architect high-performance ML systems across clusters and clouds. Covering the entire machine learning pipeline, the book offers practical guidance on data ingestion, transformation, feature engineering, and both supervised and unsupervised algorithm implementation at scale. In-depth walkthroughs demonstrate best practices for model evaluation, hyperparameter optimization, clustering, and anomaly detection—all tailored for the realities of distributed data. With dedicated chapters on automation, reproducibility, and model management, readers will learn to design robust ML pipelines, custom transformers, and orchestrate reproducible experiments using industry-standard tools. Beyond foundational topics, the book explores advanced capabilities including streaming analytics, online learning, federated privacy-preserving ML, graph-based approaches, and distributed deep learning integrations. Real-world case studies in personalization, NLP, predictive maintenance, fraud detection, and healthcare illustrate end-to-end solutions and organizational best practices. Whether deploying at web scale or tackling sensitive data environments, "Applied Machine Learning with MLlib" equips professionals with practical patterns and expert insights for building, optimizing, and maintaining state-of-the-art ML applications using Spark's powerful ecosystem.

Scala Applied Machine Learning

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Author : Pascal Bugnion
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-02-23

Scala Applied Machine Learning written by Pascal Bugnion 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 2017-02-23 with Computers categories.

Leverage the power of Scala and master the art of building, improving, and validating scalable machine learning and AI applications using Scala's most advanced and finest features About This Book Build functional, type-safe routines to interact with relational and NoSQL databases with the help of the tutorials and examples provided Leverage your expertise in Scala programming to create and customize your own scalable machine learning algorithms Experiment with different techniques; evaluate their benefits and limitations using real-world financial applications Get to know the best practices to incorporate new Big Data machine learning in your data-driven enterprise and gain future scalability and maintainability Who This Book Is For This Learning Path is for engineers and scientists who are familiar with Scala and want to learn how to create, validate, and apply machine learning algorithms. It will also benefit software developers with a background in Scala programming who want to apply machine learning. What You Will Learn Create Scala web applications that couple with JavaScript libraries such as D3 to create compelling interactive visualizations Deploy scalable parallel applications using Apache Spark, loading data from HDFS or Hive Solve big data problems with Scala parallel collections, Akka actors, and Apache Spark clusters Apply key learning strategies to perform technical analysis of financial markets Understand the principles of supervised and unsupervised learning in machine learning Work with unstructured data and serialize it using Kryo, Protobuf, Avro, and AvroParquet Construct reliable and robust data pipelines and manage data in a data-driven enterprise Implement scalable model monitoring and alerts with Scala In Detail This Learning Path aims to put the entire world of machine learning with Scala in front of you. Scala for Data Science, the first module in this course, is a tutorial guide that provides tutorials on some of the most common Scala libraries for data science, allowing you to quickly get up to speed building data science and data engineering solutions. The second course, Scala for Machine Learning guides you through the process of building AI applications with diagrams, formal mathematical notation, source code snippets, and useful tips. A review of the Akka framework and Apache Spark clusters concludes the tutorial. The next module, Mastering Scala Machine Learning, is the final step in this course. It will take your knowledge to next level and help you use the knowledge to build advanced applications such as social media mining, intelligent news portals, and more. After a quick refresher on functional programming concepts using REPL, you will see some practical examples of setting up the development environment and tinkering with data. We will then explore working with Spark and MLlib using k-means and decision trees. By the end of this course, you will be a master at Scala machine learning and have enough expertise to be able to build complex machine learning projects using Scala. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: Scala for Data Science, Pascal Bugnion Scala for Machine Learning, Patrick Nicolas Mastering Scala Machine Learning, Alex Kozlov Style and approach A tutorial with complete examples, this course will give you the tools to start building useful data engineering and data science solutions straightaway. This course provides practical examples from the field on how to correctly tackle data analysis problems, particularly for modern Big Data datasets.

Machine Learning With Apache Spark Quick Start Guide

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Author : Jillur Quddus
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-12-26

Machine Learning With Apache Spark Quick Start Guide written by Jillur Quddus 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 2018-12-26 with Computers categories.

Combine advanced analytics including Machine Learning, Deep Learning Neural Networks and Natural Language Processing with modern scalable technologies including Apache Spark to derive actionable insights from Big Data in real-time Key FeaturesMake a hands-on start in the fields of Big Data, Distributed Technologies and Machine LearningLearn how to design, develop and interpret the results of common Machine Learning algorithmsUncover hidden patterns in your data in order to derive real actionable insights and business valueBook Description Every person and every organization in the world manages data, whether they realize it or not. Data is used to describe the world around us and can be used for almost any purpose, from analyzing consumer habits to fighting disease and serious organized crime. Ultimately, we manage data in order to derive value from it, and many organizations around the world have traditionally invested in technology to help process their data faster and more efficiently. But we now live in an interconnected world driven by mass data creation and consumption where data is no longer rows and columns restricted to a spreadsheet, but an organic and evolving asset in its own right. With this realization comes major challenges for organizations: how do we manage the sheer size of data being created every second (think not only spreadsheets and databases, but also social media posts, images, videos, music, blogs and so on)? And once we can manage all of this data, how do we derive real value from it? The focus of Machine Learning with Apache Spark is to help us answer these questions in a hands-on manner. We introduce the latest scalable technologies to help us manage and process big data. We then introduce advanced analytical algorithms applied to real-world use cases in order to uncover patterns, derive actionable insights, and learn from this big data. What you will learnUnderstand how Spark fits in the context of the big data ecosystemUnderstand how to deploy and configure a local development environment using Apache SparkUnderstand how to design supervised and unsupervised learning modelsBuild models to perform NLP, deep learning, and cognitive services using Spark ML librariesDesign real-time machine learning pipelines in Apache SparkBecome familiar with advanced techniques for processing a large volume of data by applying machine learning algorithmsWho this book is for This book is aimed at Business Analysts, Data Analysts and Data Scientists who wish to make a hands-on start in order to take advantage of modern Big Data technologies combined with Advanced Analytics.

Genai On Aws

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Author : Olivier Bergeret
language : en
Publisher: John Wiley & Sons
Release Date : 2025-03-19

Genai On Aws written by Olivier Bergeret 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 2025-03-19 with Computers categories.

The definitive guide to leveraging AWS for generative AI GenAI on AWS: A Practical Approach to Building Generative AI Applications on AWS is an essential guide for anyone looking to dive into the world of generative AI with the power of Amazon Web Services (AWS). Crafted by a team of experienced cloud and software engineers, this book offers a direct path to developing innovative AI applications. It lays down a hands-on roadmap filled with actionable strategies, enabling you to write secure, efficient, and reliable generative AI applications utilizing the latest AI capabilities on AWS. This comprehensive guide starts with the basics, making it accessible to both novices and seasoned professionals. You'll explore the history of artificial intelligence, understand the fundamentals of machine learning, and get acquainted with deep learning concepts. It also demonstrates how to harness AWS's extensive suite of generative AI tools effectively. Through practical examples and detailed explanations, the book empowers you to bring your generative AI projects to life on the AWS platform. In the book, you'll: Gain invaluable insights from practicing cloud and software engineers on developing cutting-edge generative AI applications using AWS Discover beginner-friendly introductions to AI and machine learning, coupled with advanced techniques for leveraging AWS's AI tools Learn from a resource that's ideal for a broad audience, from technical professionals like cloud engineers and software developers to non-technical business leaders looking to innovate with AI Whether you're a cloud engineer, software developer, business leader, or simply an AI enthusiast, Gen AI on AWS is your gateway to mastering generative AI development on AWS. Seize this opportunity for an enduring competitive advantage in the rapidly evolving field of AI. Embark on your journey to building practical, impactful AI applications by grabbing a copy today.

Cloudera Cdp Machine Learning Engineer Exam Cdp 6001 Certification Practice 250 Questions Answer

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Author : QuickTechie | A career growth machine
language : en
Publisher: QuickTechie.com | A career growth machine
Release Date :

Cloudera Cdp Machine Learning Engineer Exam Cdp 6001 Certification Practice 250 Questions Answer written by QuickTechie | A career growth machine and has been published by QuickTechie.com | A career growth machine this book supported file pdf, txt, epub, kindle and other format this book has been release on with Business & Economics categories.

About the Book This guide provides comprehensive information for individuals preparing for the CDP Machine Learning Engineer Exam (CDP-6001). It is designed to detail the skills and knowledge required to successfully pass this certification. Audience The exam, and thus this guide, is intended for Machine Learning Engineer professionals. It covers the proficiency needed in designing, developing machine learning models using MLOps and Cloudera Machine Learning. A strong understanding of data modeling, data science concepts, deploying and tuning models is essential. Expertise in Spark, Spark MLLib, algorithms, and general machine learning is also required. Exam Details The CDP Machine Learning Engineer Exam has the following specifications: Exam Number: CDP-6001 Number of questions: 45 Duration: 90 minutes Pass Score: 60% Delivery: online, proctored. Candidates must review system requirements for online proctoring via QuestionMark. Allowed resources: None. Reference materials, white papers, user guides, or any other resources are strictly prohibited during the exam. Support: For assistance, candidates should email the provided support contact. Cloudera Skills & Knowledge Measured The exam measures skills and knowledge across several key areas, with specific weightings: Cloudera Machine Learning (31% of exam): Covers Workspaces, Projects, Experiments, Accelerators for ML Projects, Data Visualizations, Runtimes, and GPUs. Spark (18% of exam): Focuses on DataFrames, File Types, and Window Functions. Spark MLLib (22% of exam): Includes Model Selection and Tuning, Fitting and Evaluating Models, and Pipelines. Deploying a Machine Learning Model (18% of exam): Addresses Applications/API, Autoscaling and Performance, Model Metrics and Monitoring, and ML Flow. Deep Learning and General Machine Learning (11% of exam): Encompasses General Machine Learning concepts, Supervised and unsupervised learning techniques, and Algorithms.

Artificial Intelligence Algorithms Using Python

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Author : Dr Gauri M. Dhopavkar
language : en
Publisher: RK Publication
Release Date : 2024-12-31

Artificial Intelligence Algorithms Using Python written by Dr Gauri M. Dhopavkar and has been published by RK Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-31 with Computers categories.

Artificial Intelligence Algorithms Using Python the fundamentals and advanced concepts of AI algorithms through practical Python implementations. Covering machine learning, deep learning, natural language processing, and reinforcement learning, this provides a hands-on approach to building intelligent systems. It delves into algorithm design, optimization techniques, and real-world applications, making it ideal for students, researchers, and professionals. With a strong focus on code-driven learning, it enables readers to develop AI models efficiently using Python libraries such as Tensor Flow, scikit -learn, and PyTorch, bridging the gap between theoretical concepts and practical implementation.

Learn Apache Spark

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Author : Diego Rodrigues
language : en
Publisher: StudioD21
Release Date : 2025-06-25

Learn Apache Spark written by Diego Rodrigues and has been published by StudioD21 this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-25 with Business & Economics categories.

LEARN APACHE SPARK Build Scalable Pipelines with PySpark and Optimization This book is designed for students, developers, data engineers, data scientists, and technology professionals who want to master Apache Spark in practice, in corporate environments, public cloud, and modern integrations. You will learn to build scalable pipelines for large-scale data processing, orchestrating distributed workloads with AWS EMR, Databricks, Azure Synapse, and Google Cloud Dataproc. The content covers integration with Hadoop, Hive, Kafka, SQL, Delta Lake, MongoDB, and Python, as well as advanced techniques in tuning, job optimization, real-time analysis, machine learning with MLlib, and workflow automation. Includes: • Implementation of ETL and ELT pipelines with Spark SQL and DataFrames • Data streaming processing and integration with Kafka and AWS Kinesis • Optimization of distributed jobs, performance tuning, and use of Spark UI • Integration of Spark with S3, Data Lake, NoSQL, and relational databases • Deployment on managed clusters in AWS, Azure, and Google Cloud • Applied Machine Learning with MLlib, Delta Lake, and Databricks • Automation of routines, monitoring, and scalability for Big Data By the end, you will master Apache Spark as a professional solution for data analysis, process automation, and machine learning in complex, high-performance environments. apache spark, big data, pipelines, distributed processing, aws emr, databricks, streaming, etl, machine learning, cloud integration Google Data Engineer, AWS Data Analytics, Azure Data Engineer, Big Data Engineer, MLOps, DataOps Professional

Torus 2 Toward An Open Resource Using Services

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Author : Dominique Laffly
language : en
Publisher: John Wiley & Sons
Release Date : 2020-04-09

Torus 2 Toward An Open Resource Using Services written by Dominique Laffly 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 2020-04-09 with Technology & Engineering categories.

This book, presented in three volumes, examines environmental disciplines in relation to major players in contemporary science: Big Data, artificial intelligence and cloud computing. Today, there is a real sense of urgency regarding the evolution of computer technology, the ever-increasing volume of data, threats to our climate and the sustainable development of our planet. As such, we need to reduce technology just as much as we need to bridge the global socio-economic gap between the North and South; between universal free access to data (open data) and free software (open source). In this book, we pay particular attention to certain environmental subjects, in order to enrich our understanding of cloud computing. These subjects are: erosion; urban air pollution and atmospheric pollution in Southeast Asia; melting permafrost (causing the accelerated release of soil organic carbon in the atmosphere); alert systems of environmental hazards (such as forest fires, prospective modeling of socio-spatial practices and land use); and web fountains of geographical data. Finally, this book asks the question: in order to find a pattern in the data, how do we move from a traditional computing model-based world to pure mathematical research? After thorough examination of this topic, we conclude that this goal is both transdisciplinary and achievable.

Expert Strategies In Apache Spark Comprehensive Data Processing And Advanced Analytics

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Author : Adam Jones
language : en
Publisher: Walzone Press
Release Date : 2025-01-03

Expert Strategies In Apache Spark Comprehensive Data Processing And Advanced Analytics written by Adam Jones and has been published by Walzone Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-03 with Computers categories.

"Expert Strategies in Apache Spark: Comprehensive Data Processing and Advanced Analytics" is an essential guide for data professionals aiming to master Apache Spark's sophisticated capabilities. Building on foundational knowledge, this book delves into expert-level data processing and advanced analytics techniques. It provides detailed insights into Spark’s core components like RDDs, DataFrames, and Datasets, while also exploring cutting-edge features such as MLlib for machine learning and GraphX for graph processing. Through comprehensive and practical chapters, readers will learn to optimize Spark queries using Catalyst and Tungsten, efficiently handle streaming data, manage Spark clusters, and fine-tune performance for complex applications. Whether you're a data engineer looking to optimize Spark deployments or a data scientist aiming to enhance analytical models, this book delivers the expert strategies and best practices needed to tackle big data challenges and extract actionable insights at scale. Unlock your potential in the dynamic world of big data with "Expert Strategies in Apache Spark: Comprehensive Data Processing and Advanced Analytics". Harness the full potential of your data with Spark's advanced functionalities and transform your data operations into impactful intelligence.

Scala For Data Science

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Author : Pascal Bugnion
language : en
Publisher: Packt Publishing Ltd
Release Date : 2016-01-28

Scala For Data Science written by Pascal Bugnion 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 2016-01-28 with Computers categories.

Leverage the power of Scala with different tools to build scalable, robust data science applications About This Book A complete guide for scalable data science solutions, from data ingestion to data visualization Deploy horizontally scalable data processing pipelines and take advantage of web frameworks to build engaging visualizations Build functional, type-safe routines to interact with relational and NoSQL databases with the help of tutorials and examples provided Who This Book Is For If you are a Scala developer or data scientist, or if you want to enter the field of data science, then this book will give you all the tools you need to implement data science solutions. What You Will Learn Transform and filter tabular data to extract features for machine learning Implement your own algorithms or take advantage of MLLib's extensive suite of models to build distributed machine learning pipelines Read, transform, and write data to both SQL and NoSQL databases in a functional manner Write robust routines to query web APIs Read data from web APIs such as the GitHub or Twitter API Use Scala to interact with MongoDB, which offers high performance and helps to store large data sets with uncertain query requirements Create Scala web applications that couple with JavaScript libraries such as D3 to create compelling interactive visualizations Deploy scalable parallel applications using Apache Spark, loading data from HDFS or Hive In Detail Scala is a multi-paradigm programming language (it supports both object-oriented and functional programming) and scripting language used to build applications for the JVM. Languages such as R, Python, Java, and so on are mostly used for data science. It is particularly good at analyzing large sets of data without any significant impact on performance and thus Scala is being adopted by many developers and data scientists. Data scientists might be aware that building applications that are truly scalable is hard. Scala, with its powerful functional libraries for interacting with databases and building scalable frameworks will give you the tools to construct robust data pipelines. This book will introduce you to the libraries for ingesting, storing, manipulating, processing, and visualizing data in Scala. Packed with real-world examples and interesting data sets, this book will teach you to ingest data from flat files and web APIs and store it in a SQL or NoSQL database. It will show you how to design scalable architectures to process and modelling your data, starting from simple concurrency constructs such as parallel collections and futures, through to actor systems and Apache Spark. As well as Scala's emphasis on functional structures and immutability, you will learn how to use the right parallel construct for the job at hand, minimizing development time without compromising scalability. Finally, you will learn how to build beautiful interactive visualizations using web frameworks. This book gives tutorials on some of the most common Scala libraries for data science, allowing you to quickly get up to speed with building data science and data engineering solutions. Style and approach A tutorial with complete examples, this book will give you the tools to start building useful data engineering and data science solutions straightaway