Data Preparation For Machine Learning

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Data Preparation For Machine Learning

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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2020-06-30

Data Preparation For Machine Learning written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-30 with Computers categories.

Data preparation involves transforming raw data in to a form that can be modeled using machine learning algorithms. Cut through the equations, Greek letters, and confusion, and discover the specialized data preparation techniques that you need to know to get the most out of your data on your next project. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover how to confidently and effectively prepare your data for predictive modeling with machine learning.

Data Preprocessing With Python For Absolute Beginners

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Author : A. I. Sciences OU
language : en
Publisher:
Release Date : 2021-03-25

Data Preprocessing With Python For Absolute Beginners written by A. I. Sciences OU and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-25 with categories.

This book is dedicated to data preparation and explains how to perform different data preparation techniques on various datasets using different data preparation libraries written in the Python programming language.Key Features* A crash course in Python to fill any gaps in prerequisite knowledge and a solid foundation on which to build your new skills* A complete data preparation pipeline for your guided practice* Three real-world projects covering each major task to cement your learned skills in data preparation, classification, and regressionBook DescriptionThe book follows a straightforward approach. It is divided into nine chapters. Chapter 1 introduces the basic concept of data preparation and installation steps for the software that we will need to perform data preparation in this book. Chapter 1 also contains a crash course on Python, followed by a brief overview of different data types in Chapter 2. You will then learn how to handle missing values in the data, while the categorical encoding of numeric data is explained in Chapter 4.The second half of the course presents data discretization and describes the handling of outliers' process. Chapter 7 demonstrates how to scale features in the dataset. Subsequent chapters teach you to handle mixed and DateTime data type, balance data, and practice resampling. A full data preparation final project is also available at the end of the book.Different types of data preprocessing techniques have been explained theoretically, followed by practical examples in each chapter. Each chapter also contains an exercise that students can use to evaluate their understanding of the chapter's concepts. By the end of this course, you will have built a solid working knowledge in data preparation--the first steps to any data science or machine learning career and an essential skillset for any aspiring developer.The code bundle for this course is available at https://www.aispublishing.net/book-data-preprocessingWhat you will learn* Explore different libraries for data preparation* Understand data types* Handle missing data* Encode categorical data* Discretize data* Learn to handle outliers* Practice feature scaling* Handle mixed and DateTime variables and imbalanced datasets* Employ your new skills to complete projects in data preparation, classification, and regressionWho this book is forIn addition to beginners in data preparation with Python, this book can also be used as a reference manual by intermediate and experienced programmers. It contains data preprocessing code samples using multiple data visualization libraries.

Data Preparation For Data Mining

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Author : Dorian Pyle
language : en
Publisher: Morgan Kaufmann
Release Date : 1999-03-22

Data Preparation For Data Mining written by Dorian Pyle and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999-03-22 with Computers categories.

This book focuses on the importance of clean, well-structured data as the first step to successful data mining. It shows how data should be prepared prior to mining in order to maximize mining performance.

Mastering Ai Model Training

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Author : Cybellium
language : en
Publisher: Cybellium Ltd
Release Date : 2023-09-05

Mastering Ai Model Training written by Cybellium and has been published by Cybellium Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-05 with Computers categories.

Are you ready to take your AI training skills to the next level? In "Mastering AI Model Training" by Kris Hermans, you'll embark on a transformative journey that will empower you to train highly accurate and efficient artificial intelligence models. Uncover Advanced Techniques and Best Practices As AI continues to revolutionize industries, the ability to train powerful and optimized models is paramount. In this comprehensive guide, Kris Hermans reveals the secrets to mastering AI model training. Explore advanced techniques, cutting-edge algorithms, and industry best practices that will propel your AI training expertise to new heights. Become an Expert in Training AI Models Whether you're a seasoned data scientist or a passionate AI enthusiast, this book provides a structured approach to mastering AI model training. Kris Hermans demystifies complex concepts and presents them in a clear and practical manner. Through real-world examples and hands-on exercises, you'll develop the skills and intuition necessary to train AI models that achieve exceptional performance. From Fundamentals to Advanced Topics "Mastering AI Model Training" covers the full spectrum of AI training, starting from the basics of data preprocessing and feature engineering and progressing to advanced topics such as transfer learning, hyperparameter optimization, and model compression. Gain a deep understanding of different training algorithms and architectures, and learn how to adapt them to various domains and use cases. Optimize Training for Performance and Efficiency Discover strategies for improving model performance, reducing training time, and optimizing resource utilization. Explore techniques for handling large datasets, distributed training, and leveraging hardware accelerators such as GPUs and TPUs. With Kris Hermans as your guide, you'll learn how to train models that deliver superior results while maximizing computational efficiency. Practical Applications and Real-World Case Studies Immerse yourself in practical applications of AI model training across industries such as healthcare, finance, manufacturing, and more. Dive into captivating case studies that demonstrate how AI training is transforming businesses and driving innovation. Gain insights into the challenges faced by organizations and learn how they leverage AI training techniques to gain a competitive edge. Ethical Considerations and Responsible AI With great power comes great responsibility. "Mastering AI Model Training" addresses the ethical considerations associated with AI training and highlights the importance of responsible AI practices. Learn how to mitigate biases, ensure fairness, and navigate ethical challenges to build AI models that are not only accurate and efficient but also ethical and trustworthy.

Simplifying Data Preparation For Machine Learning On Tabular Data

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Author : Vraj Shah
language : en
Publisher:
Release Date : 2022

Simplifying Data Preparation For Machine Learning On Tabular Data written by Vraj Shah and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.

Machine learning (ML) over tabular data has become ubiquitous with applications in many domains. This success has led to the rise of ML platforms, including automated ML (AutoML) platforms to manage the end-to-end ML workflow. The tedious grunt work involved in data preparation (prep) reduces data scientist productivity and slows down the ML development lifecycle, which makes the automation of data prep even more critical. While many works have looked into feature engineering and model selection in the end-to-end ML workflows, little attention has been paid towards understanding data prep and its utility for ML. Also, automating data prep remains challenging due to several reasons such as semantic gaps and lack of ways to objectively measure accuracy. In this dissertation, we take a step towards addressing such challenges using database schema management and ML techniques to simplify, better automate, and understand the utility of ML data prep. We create new benchmark datasets, methodology for benchmarking and automating ML data prep, and devise novel empirical analyses to characterize the significance of critical data prep steps. Our work presents several critical artifacts that not only provide a systematic approach to reduce grunt work and improve the productivity of ML practitioners but also can help establish the science of building (Auto)ML platforms. Our work opens up several new research directions at the intersection of ML, data management, and ML system design.

Ai Driven Data Engineering Transforming Big Data Into Actionable Insight

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Author : Eswar Prasad Galla
language : en
Publisher: JEC PUBLICATION
Release Date :

Ai Driven Data Engineering Transforming Big Data Into Actionable Insight written by Eswar Prasad Galla and has been published by JEC PUBLICATION this book supported file pdf, txt, epub, kindle and other format this book has been release on with Architecture categories.

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A Study On Next Generation Materials And Devices

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Author : M. S. Vijaya Kumar
language : en
Publisher: CRC Press
Release Date : 2025-09-29

A Study On Next Generation Materials And Devices written by M. S. Vijaya Kumar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-29 with Technology & Engineering categories.

A Study on Next-Generation Materials and Devices proudly presents the proceedings of the International Conference on Next-Generation Materials and Devices (ICNMD, 2024) held from August 01–03, 2024, in Virudhunagar, India. ICNMD 2024 served as a crucial platform, focusing on state-of-the-art research and development in A Study on Next-Generation Materials and Devices for sustainable development. The diverse program explored major topics such as energy solutions, environmental concerns, advanced sensors, the role of artificial intelligence, and computational approaches for materials design. It also delved into biomaterials for medical applications, alongside discussions on next-generation semiconductors, and flexible electronics poised to revolutionize the electronics industry. The event covered all the significant verticals related to materials and devices, featuring pioneers who shed light on uncharted domains.

Mastering Ai App Development With Mern Stack Step Into The Future Of App Development By Building Intelligent Ai Powered Applications With Mern Stack And Tensorflow Js For Seamless User Experiences

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Author : Anik Acharjee
language : en
Publisher: Orange Education Pvt Limited
Release Date : 2024-11-05

Mastering Ai App Development With Mern Stack Step Into The Future Of App Development By Building Intelligent Ai Powered Applications With Mern Stack And Tensorflow Js For Seamless User Experiences written by Anik Acharjee and has been published by Orange Education Pvt Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-05 with Computers categories.

Transform Your Web App Development Journey with MERN and AI Key Features● Utilize AI for code generation, debugging, and optimizing performance in MERN applications.● Build AI-powered web apps with real-time data processing and user behavior insights.● Integrate AI capabilities seamlessly with MongoDB, Express.js, React, and Node.js for scalable web solutions. Book DescriptionWith AI applications driving a projected $15.7 trillion boost to the global economy by 2030, combining AI with the popular MERN stack has become a game-changer for developers and businesses alike. Mastering AI App Development with MERN Stack is a hands-on guide designed for developers ready to bring AI capabilities to their MERN applications, covering everything from foundational machine learning to advanced, real-world solutions. Starting with the essentials of setting up a MERN development environment, the book guides readers through machine learning basics in JavaScript, enabling AI integration with Node.js and TensorFlow.js. Each chapter provides practical insights into building intelligent interfaces with React, effective data handling with MongoDB, and AI middleware using Express.js. Readers will learn to create features like AI-powered chatbots, image and voice recognition, and personalized recommendation systems. Real-world scenarios and case studies demonstrate how AI can elevate MERN applications. With guidance on security practices, deployment, and scaling, this book is a complete toolkit for building secure, production-ready AI solutions with MERN. Mastering AI with the MERN Stack empowers developers to unlock the full potential of AI in the MERN ecosystem, creating innovative, impactful applications for an AI-driven world. What you will learn● Integrate AI into MERN applications for improved user experiences.● Build AI-powered web apps using the MERN stack effectively.● Implement real-time data processing and personalized content features.● Leverage pre-trained AI models for language and analytics tasks.● Design scalable AI architectures to enhance performance and capacity. Table of Contents1. Introduction to AI and the MERN Ecosystem2. Setting Up the MERN Development Environment3. Fundamentals of Machine Learning with JavaScript4. Implementing AI with Node.js and TensorFlow.js5. Creating Intelligent User Interfaces with React6. Data Management for AI with MongoDB7. Building AI Middleware with Express.js8. Crafting AI-Powered Chatbots9. Image and Voice Recognition Capabilities10. Personalization with Recommendation Systems11. Deploying MERN and AI Applications12. Security Practices for AI-Enabled MERN Applications13. Scaling AI Features in Production14. Emerging Trends in AI and MERN Development15. Case Studies and Real-World Success Stories Index

Data Preparation Of Machine Learning

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Author : Mike Data
language : en
Publisher:
Release Date : 2021-06-05

Data Preparation Of Machine Learning written by Mike Data and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-05 with categories.

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Applied Text Mining

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Author : Usman Qamar
language : en
Publisher: Springer Nature
Release Date : 2024-06-10

Applied Text Mining written by Usman Qamar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-10 with Computers categories.

This textbook covers the concepts, theories, and implementations of text mining and natural language processing (NLP). It covers both the theory and the practical implementation, and every concept is explained with simple and easy-to-understand examples. It consists of three parts. In Part 1 which consists of three chapters details about basic concepts and applications of text mining are provided, including eg sentiment analysis and opinion mining. It builds a strong foundation for the reader in order to understand the remaining parts. In the five chapters of Part 2, all the core concepts of text analytics like feature engineering, text classification, text clustering, text summarization, topic mapping, and text visualization are covered. Finally, in Part 3 there are three chapters covering deep-learning-based text mining, which is the dominating method applied to practically all text mining tasks nowadays. Various deep learning approaches to text mining are covered, includingmodels for processing and parsing text, for lexical analysis, and for machine translation. All three parts include large parts of Python code that shows the implementation of the described concepts and approaches. The textbook was specifically written to enable the teaching of both basic and advanced concepts from one single book. The implementation of every text mining task is carefully explained, based Python as the programming language and Spacy and NLTK as Natural Language Processing libraries. The book is suitable for both undergraduate and graduate students in computer science and engineering.