Practical Automated Machine Learning Using H2o Ai
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Practical Automated Machine Learning Using H2o Ai
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Author : Salil Ajgaonkar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-09-26
Practical Automated Machine Learning Using H2o Ai written by Salil Ajgaonkar 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 2022-09-26 with Computers categories.
Accelerate the adoption of machine learning by automating away the complex parts of the ML pipeline using H2O.ai Key FeaturesLearn how to train the best models with a single click using H2O AutoMLGet a simple explanation of model performance using H2O ExplainabilityEasily deploy your trained models to production using H2O MOJO and POJOBook Description With the huge amount of data being generated over the internet and the benefits that Machine Learning (ML) predictions bring to businesses, ML implementation has become a low-hanging fruit that everyone is striving for. The complex mathematics behind it, however, can be discouraging for a lot of users. This is where H2O comes in – it automates various repetitive steps, and this encapsulation helps developers focus on results rather than handling complexities. You'll begin by understanding how H2O's AutoML simplifies the implementation of ML by providing a simple, easy-to-use interface to train and use ML models. Next, you'll see how AutoML automates the entire process of training multiple models, optimizing their hyperparameters, as well as explaining their performance. As you advance, you'll find out how to leverage a Plain Old Java Object (POJO) and Model Object, Optimized (MOJO) to deploy your models to production. Throughout this book, you'll take a hands-on approach to implementation using H2O that'll enable you to set up your ML systems in no time. By the end of this H2O book, you'll be able to train and use your ML models using H2O AutoML, right from experimentation all the way to production without a single need to understand complex statistics or data science. What you will learnGet to grips with H2O AutoML and learn how to use itExplore the H2O Flow Web UIUnderstand how H2O AutoML trains the best models and automates hyperparameter optimizationFind out how H2O Explainability helps understand model performanceExplore H2O integration with scikit-learn, the Spring Framework, and Apache StormDiscover how to use H2O with Spark using H2O Sparkling WaterWho this book is for This book is for engineers and data scientists who want to quickly adopt machine learning into their products without worrying about the internal intricacies of training ML models. If you're someone who wants to incorporate machine learning into your software system but don't know where to start or don't have much expertise in the domain of ML, then you'll find this book useful. Basic knowledge of statistics and programming is beneficial. Some understanding of ML and Python will be helpful.
Practical Guide To H2o Ai
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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-05-31
Practical Guide To H2o Ai 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 2025-05-31 with Computers categories.
"Practical Guide to H2O.ai" "Practical Guide to H2O.ai" is a comprehensive resource designed for data scientists, machine learning engineers, and IT professionals who seek to master the full capabilities of H2O.ai’s powerful platform. This guide delivers a deep dive into the architecture and components of the H2O ecosystem—including H2O-3 and Driverless AI—while demystifying its integration within diverse enterprise environments, whether on-premises, cloud, or hybrid. Readers will gain actionable insights into secure system deployment, cluster management, large-scale data ingestion, and optimized ETL workflows, ensuring robust infrastructure that meets the demands of modern data-driven organizations. Structured to support both practical adoption and technical excellence, the book traverses core machine learning tasks, from advanced preprocessing and feature engineering to supervised and unsupervised learning with leading algorithms such as GBM, XGBoost, and deep neural networks. Special emphasis is placed on scalable automation through H2O AutoML, presenting real-world case studies while showcasing best practices in algorithm selection, hyperparameter optimization, and model evaluation. Dedicated chapters explore explainable AI and responsible ML practices—covering interpretability, bias mitigation, compliance, and data privacy—empowering readers to build transparent, auditable, and trustworthy solutions for complex, regulated domains. With detailed coverage of emerging fields like natural language processing, time series analysis, MLOps, and distributed deep learning, "Practical Guide to H2O.ai" is an indispensable reference for leveraging H2O.ai at scale. Topics such as advanced model deployment, real-time inference, CI/CD integration, and production troubleshooting combine theory with hands-on strategies for operationalizing machine learning workflows. Whether you are scaling to petabyte data, orchestrating containerized clusters, or exploring cutting-edge areas like federated learning and edge ML, this guide equips you with the knowledge and tools to drive innovation and achieve enterprise-level AI success.
Automated Machine Learning
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Author : Frank Hutter
language : en
Publisher: Springer
Release Date : 2019-05-17
Automated Machine Learning written by Frank Hutter and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-17 with Computers categories.
This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.
Advanced Analytics And Machine Learning For Business Growth A Practical Guide
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Author : Vybhav Reddy Kammireddy Changalreddy Prof Ashok Kumar
language : en
Publisher: DeepMisti Publication
Release Date : 2025-01-15
Advanced Analytics And Machine Learning For Business Growth A Practical Guide written by Vybhav Reddy Kammireddy Changalreddy Prof Ashok Kumar and has been published by DeepMisti Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-15 with Computers categories.
In today’s competitive business landscape, growth is no longer just about hard work and intuition—it’s about making smarter decisions driven by insights from data. The advent of advanced analytics and machine learning (ML) has revolutionized how businesses understand their markets, optimize operations, and delight customers. Yet, the journey from raw data to actionable insights can be daunting, especially for organizations new to these transformative technologies. Advanced Analytics and Machine Learning for Business Growth: A Practical Guide is designed to demystify the complex world of analytics and machine learning, providing business leaders, analysts, and data enthusiasts with a clear and actionable roadmap to drive tangible results. This book bridges the gap between the technical depth of data science and the practical demands of business strategy, empowering readers to unlock the full potential of their data. Inside, you’ll discover: • The foundational principles of advanced analytics and machine learning in business contexts. • Practical applications for solving real-world challenges such as customer segmentation, churn prediction, and demand forecasting. • Step-by-step guides for implementing machine learning models, from data preparation to deployment. • Tools, platforms, and frameworks that accelerate analytics workflows. • Insights into navigating challenges like data quality, organizational buy-in, and ethical AI practices. This book is intentionally practical. Instead of overwhelming you with complex theories and algorithms, it focuses on actionable strategies that you can implement immediately, whether you’re working in retail, finance, healthcare, manufacturing, or any other industry. As you turn the pages, you’ll discover how advanced analytics and machine learning can transform your business—from identifying hidden opportunities to solving critical challenges. More importantly, you’ll gain the confidence to lead data-driven initiatives that foster growth, resilience, and competitive advantage. The future belongs to businesses that can adapt and innovate. With this book as your guide, you’re equipped to harness the power of advanced analytics and machine learning to fuel your organization’s growth and success. Welcome to the journey of transforming your business with data. Let’s build the future, one insight at a time. Authors
Interpretable Machine Learning
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Author : Christoph Molnar
language : en
Publisher: Lulu.com
Release Date : 2020
Interpretable Machine Learning written by Christoph Molnar and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computers categories.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Mlops In Practice
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Author : Diego Rodrigues
language : en
Publisher: StudioD21
Release Date : 2025-02-11
Mlops In Practice 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 2025-02-11 with Business & Economics categories.
MLOps IN PRACTICE is an essential guide for professionals looking to take Machine Learning models from experimentation to production with efficiency, scalability, and continuous automation. In this book, you will learn how to implement robust pipelines, monitor AI models in real time, and apply the best MLOps practices to ensure performance, reliability, and governance in Artificial Intelligence projects. Written by Diego Rodrigues, a best-selling author with over 180 titles published in six languages, this book combines theory and practice, offering a modern and applied approach to the current MLOps landscape. Throughout the chapters, you will explore essential frameworks and tools such as Docker, Kubernetes, CI/CD for Machine Learning, MLflow, TensorFlow Extended (TFX), FastAPI, and more. You will learn how to: Automate and scale Machine Learning pipelines with advanced versioning and monitoring techniques. Implement CI/CD for AI models, ensuring continuous training, deployment, and retraining. Manage models in production by applying observability, traceability, and bias mitigation practices. Utilize leading industry tools such as Kubeflow, MLflow, Airflow, and TFX to orchestrate ML workflows. Enhance AI governance and security, ensuring compliance with regulations and international standards. With practical examples, case studies, and established frameworks, MasterTech: MLOps in Practice is not just a technical manual—it is an indispensable resource for data scientists, ML engineers, software architects, and technology leaders looking to implement MLOps strategically and at scale. Get ready to revolutionize the way you manage AI models in production and master the most advanced MLOps techniques in 2025! TAGS: Python Java Linux Kali HTML ASP.NET Ada Assembly BASIC Borland Delphi C C# C++ CSS Cobol Compilers DHTML Fortran General JavaScript LISP PHP Pascal Perl Prolog RPG Ruby SQL Swift UML Elixir Haskell VBScript Visual Basic XHTML XML XSL Django Flask Ruby on Rails Angular React Vue.js Node.js Laravel Spring Hibernate .NET Core Express.js TensorFlow PyTorch Jupyter Notebook Keras Bootstrap Foundation jQuery SASS LESS Scala Groovy MATLAB R Objective-C Rust Go Kotlin TypeScript Dart SwiftUI Xamarin React Native NumPy Pandas SciPy Matplotlib Seaborn D3.js OpenCV NLTK PySpark BeautifulSoup Scikit-learn XGBoost CatBoost LightGBM FastAPI Redis RabbitMQ Kubernetes Docker Jenkins Terraform Ansible Vagrant GitHub GitLab CircleCI Regression Logistic Regression Decision Trees Random Forests AI ML K-Means Clustering Support Vector Machines Gradient Boosting Neural Networks LSTMs CNNs GANs ANDROID IOS MACOS WINDOWS Nmap Metasploit Framework Wireshark Aircrack-ng John the Ripper Burp Suite SQLmap Maltego Autopsy Volatility IDA Pro OllyDbg YARA Snort ClamAV Netcat Tcpdump Foremost Cuckoo Sandbox Fierce HTTrack Kismet Hydra Nikto OpenVAS Nessus ZAP Radare2 Binwalk GDB OWASP Amass Dnsenum Dirbuster Wpscan Responder Setoolkit Searchsploit Recon-ng BeEF AWS Google Cloud IBM Azure Databricks Nvidia Meta Power BI IoT CI/CD Hadoop Spark Dask SQLAlchemy Web Scraping MySQL Big Data Science OpenAI ChatGPT Handler RunOnUiThread() Qiskit Q# Cassandra Bigtable VIRUS MALWARE Information Pen Test Cybersecurity Linux Distributions Ethical Hacking Vulnerability Analysis System Exploration Wireless Attacks Web Application Security Malware Analysis Social Engineering Social Engineering Toolkit SET Computer Science IT Professionals Careers Expertise Library Training Operating Systems Security Testing Penetration Test Cycle Mobile Techniques Industry Global Trends Tools Framework Network Security Courses Tutorials Challenges Landscape Cloud Threats Compliance Research Technology Flutter Ionic Web Views Capacitor APIs REST GraphQL Firebase Redux Provider Bitrise Actions Material Design Cupertino Fastlane Appium Selenium Jest Visual Studio AR VR sql deepseek mysql startup digital marketing
Hands On Machine Learning With R
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Author : Brad Boehmke
language : en
Publisher: CRC Press
Release Date : 2019-11-07
Hands On Machine Learning With R written by Brad Boehmke and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-07 with Business & Economics categories.
Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering, resampling, deep learning and more. · Uses a hands-on approach and real world data.
Machine Learning Made Simple
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Author : Daniel Lee
language : en
Publisher: Daniel Lee
Release Date : 2025-01-22
Machine Learning Made Simple written by Daniel Lee and has been published by Daniel Lee this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-22 with Computers categories.
"Machine Learning Made Simple: A Practical Introduction: Building Intelligent Algorithms from Scratch" is your go-to resource for comprehending and using machine learning without becoming bogged down in overwhelming complexity or technical jargon. This book, which simplifies the principles of machine learning and provides practical possibilities to develop clever algorithms step-by-step, is ideal for novices and inquisitive learners. The book begins by outlining the fundamental ideas, including supervised and unsupervised learning, and then it progressively guides you through the crucial steps involved in managing data, including cleaning, preprocessing, and visualizing it in order to derive insightful information. You will gain a comprehension of the theory and the ability to apply it on your own by learning how to build fundamental algorithms like linear regression from scratch with an emphasis on practicality. The book provides real-world examples and a case study where you will construct and assess a basic prediction model to further humanize the concepts. As you advance, you'll also discover how to enhance model performance and switch to specialized tools like Scikit-learn, which will allow you to expand your knowledge and skills. This book will enable you to understand the principles and begin developing clever solutions, regardless of whether you're a professional, tech enthusiast, or student interested in machine learning. Take the first step toward becoming an expert in machine learning by diving right in!
Practical Big Data Analytics
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Author : Nataraj Dasgupta
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-01-15
Practical Big Data Analytics written by Nataraj Dasgupta 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 2018-01-15 with Computers categories.
Get command of your organizational Big Data using the power of data science and analytics Key Features A perfect companion to boost your Big Data storing, processing, analyzing skills to help you take informed business decisions Work with the best tools such as Apache Hadoop, R, Python, and Spark for NoSQL platforms to perform massive online analyses Get expert tips on statistical inference, machine learning, mathematical modeling, and data visualization for Big Data Book Description Big Data analytics relates to the strategies used by organizations to collect, organize and analyze large amounts of data to uncover valuable business insights that otherwise cannot be analyzed through traditional systems. Crafting an enterprise-scale cost-efficient Big Data and machine learning solution to uncover insights and value from your organization's data is a challenge. Today, with hundreds of new Big Data systems, machine learning packages and BI Tools, selecting the right combination of technologies is an even greater challenge. This book will help you do that. With the help of this guide, you will be able to bridge the gap between the theoretical world of technology with the practical ground reality of building corporate Big Data and data science platforms. You will get hands-on exposure to Hadoop and Spark, build machine learning dashboards using R and R Shiny, create web-based apps using NoSQL databases such as MongoDB and even learn how to write R code for neural networks. By the end of the book, you will have a very clear and concrete understanding of what Big Data analytics means, how it drives revenues for organizations, and how you can develop your own Big Data analytics solution using different tools and methods articulated in this book. What you will learn - Get a 360-degree view into the world of Big Data, data science and machine learning - Broad range of technical and business Big Data analytics topics that caters to the interests of the technical experts as well as corporate IT executives - Get hands-on experience with industry-standard Big Data and machine learning tools such as Hadoop, Spark, MongoDB, KDB+ and R - Create production-grade machine learning BI Dashboards using R and R Shiny with step-by-step instructions - Learn how to combine open-source Big Data, machine learning and BI Tools to create low-cost business analytics applications - Understand corporate strategies for successful Big Data and data science projects - Go beyond general-purpose analytics to develop cutting-edge Big Data applications using emerging technologies Who this book is for The book is intended for existing and aspiring Big Data professionals who wish to become the go-to person in their organization when it comes to Big Data architecture, analytics, and governance. While no prior knowledge of Big Data or related technologies is assumed, it will be helpful to have some programming experience.
Mastering Automated Machine Learning Concepts Tools And Techniques
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Author : Peter Jones
language : en
Publisher: Walzone Press
Release Date : 2025-01-17
Mastering Automated Machine Learning Concepts Tools And Techniques written by Peter 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 2025-01-17 with Computers categories.
"Mastering Automated Machine Learning: Concepts, Tools, and Techniques" is an essential guide for anyone seeking to unlock the full potential of Automated Machine Learning (AutoML), a groundbreaking technology transforming the field of data science. By automating complex and time-consuming processes, AutoML is making machine learning more efficient and accessible to a broader range of professionals. This book offers an in-depth exploration of core principles, state-of-the-art methodologies, and the practical tools that define AutoML. From data preparation and feature engineering to model selection, tuning, and deployment, readers will acquire a thorough understanding of how AutoML streamlines the entire machine learning pipeline. Whether you're a data scientist, machine learning engineer, or software developer eager to harness the power of automation, "Mastering Automated Machine Learning" provides the insights you need to implement cutting-edge AutoML solutions. With practical examples and guidance on using Python-based frameworks, this book equips you to revolutionize your data science projects. Embrace the future of machine learning and optimize your workflows with "Mastering Automated Machine Learning: Concepts, Tools, and Techniques."