Data Preparation Of 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 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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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.
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.
Data Preprocessing With Python For Absolute Beginners
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Author : Ai Publishing
language : en
Publisher:
Release Date : 2020-03-21
Data Preprocessing With Python For Absolute Beginners written by Ai Publishing and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-21 with categories.
Are you looking for a hands-on approach to learn Data Preprocessing techniques fast? Do you need to start learning Python for Data Preparation from Scratch? This book is for you.This book is dedicated to data preparation and explains how to perform different data preparation techniques on a variety of datasets using various data preparation libraries written in the Python programming language. It is suggested that you use this book for data preparation purposes only and not for data science or machine learning. For the application of data preparation in data science and machine learning, read this book in conjunction with dedicated books on machine learning and data science. This book explains the process of data preparation using various libraries from scratch. All the codes and datasets have been provided. However, to download data preparation libraries, you will need the internet. In addition to beginners to data preparation with Python, this book can also be used as a reference manual by intermediate and experienced programmers as it contains data preparation code samples using multiple data visualization libraries. What this book offers... The book follows a very simple approach. It is divided into nine chapters. Chapter 1 introduces the basic concept of data preparation, along with the 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. A brief overview of different data types is given in Chapter 2. Chapter 3 explains how to handle missing values in the data, while the categorical encoding of numeric data is explained in Chapter 4. Data discretization is presented in Chapter 5. Chapter 6 explains the process of handline outliers, while Chapter 7 explains how to scale features in the dataset. Handling of mixed and datetime data type is explained in Chapter 8, while data balancing and resampling has been explained in Chapter 9. A full data preparation final project is also available at the end of the book. In each chapter, different types of data preparation techniques have been explained theoretically, followed by practical examples. Each chapter also contains an exercise that students can use to evaluate their understanding of the concepts explained in the chapter.Clear and Easy to Understand SolutionsAll solutions in this book are extensively tested by a group of beta readers. The solutions provided are simplified as much as possible so that they can serve as examples for you to refer to when you are learning a new skill.Topics Covered: What Is Data Preparation Python Crash Course Different Libraries for Data Preparation Understanding Data Types Handling Missing Data Encoding Categorical Data Data Discretization Outlier Handling Feature Scaling Handling Mixed and DateTime Variables Handling Imbalanced Datasets A Complete Data Preparation Pipeline Project 1 - Data Preparation Project 2 - Classification Project Project 3 - Regression Project Click the BUY button and download the book now to start learning Data Preprocessing Using Python.
Data Preparation Untuk Machine Learning Deep Learning
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Author : Dr. Ir. Rianto, M.Eng., IPM., ASEAN Eng.
language : id
Publisher: Penerbit Andi
Release Date : 2025-01-10
Data Preparation Untuk Machine Learning Deep Learning written by Dr. Ir. Rianto, M.Eng., IPM., ASEAN Eng. and has been published by Penerbit Andi this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-10 with Computers categories.
Data Preparation untuk Machine Learning & Deep Learning menawarkan panduan komprehensif untuk memahami dan menerapkan langkah- langkah persiapan data yang esensial dalam membangun model pembelajaran mesin yang akurat. Dalam analisis data, data preparation adalah tahap krusial yang sering kali menentukan keberhasilan model, baik dalam Machine Learning maupun Deep Learning. Buku ini membahas beragam teknik penting, termasuk data cleaning, data transformation, feature engineering, dan data augmentation untuk data teks, gambar, serta time series. Setiap bab disusun secara sistematis untuk memberikan pemahaman teoritis yang mendalam, dilengkapi dengan contoh penerapan nyata dan kode program Python. Buku ini juga mengupas strategi praktis untuk mengatasi berbagai tantangan, seperti penanganan data tidak lengkap, deteksi anomali, hingga pengelompokan data yang optimal. Dilengkapi dengan studi kasus dan metode evaluasi, buku ini memberikan panduan efektif bagi pembaca yang ingin menerapkan data preparation dalam riset maupun aplikasi industri. Dengan pendekatan terstruktur dan praktis, buku ini dirancang untuk mahasiswa, praktisi, hingga peneliti yang ingin memperdalam pemahaman tentang persiapan data untuk model pembelajaran mesin
Machine Learning With R
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Author : Brett Lantz
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-05-29
Machine Learning With R written by Brett Lantz 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 2023-05-29 with Computers categories.
Use R and tidyverse to prepare, clean, import, visualize, transform, program, communicate, predict and model data No R experience is required, although prior exposure to statistics and programming is helpful Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Get to grips with the tidyverse, challenging data, and big data Create clear and concise data and model visualizations that effectively communicate results to stakeholders Solve a variety of problems using regression, ensemble methods, clustering, deep learning, probabilistic models, and more Book DescriptionDive into R with this data science guide on machine learning (ML). Machine Learning with R, Fourth Edition, takes you through classification methods like nearest neighbor and Naive Bayes and regression modeling, from simple linear to logistic. Dive into practical deep learning with neural networks and support vector machines and unearth valuable insights from complex data sets with market basket analysis. Learn how to unlock hidden patterns within your data using k-means clustering. With three new chapters on data, you’ll hone your skills in advanced data preparation, mastering feature engineering, and tackling challenging data scenarios. This book helps you conquer high-dimensionality, sparsity, and imbalanced data with confidence. Navigate the complexities of big data with ease, harnessing the power of parallel computing and leveraging GPU resources for faster insights. Elevate your understanding of model performance evaluation, moving beyond accuracy metrics. With a new chapter on building better learners, you’ll pick up techniques that top teams use to improve model performance with ensemble methods and innovative model stacking and blending techniques. Machine Learning with R, Fourth Edition, equips you with the tools and knowledge to tackle even the most formidable data challenges. Unlock the full potential of machine learning and become a true master of the craft.What you will learn Learn the end-to-end process of machine learning from raw data to implementation Classify important outcomes using nearest neighbor and Bayesian methods Predict future events using decision trees, rules, and support vector machines Forecast numeric data and estimate financial values using regression methods Model complex processes with artificial neural networks Prepare, transform, and clean data using the tidyverse Evaluate your models and improve their performance Connect R to SQL databases and emerging big data technologies such as Spark, Hadoop, H2O, and TensorFlow Who this book is for This book is designed to help data scientists, actuaries, data analysts, financial analysts, social scientists, business and machine learning students, and any other practitioners who want a clear, accessible guide to machine learning with R. No R experience is required, although prior exposure to statistics and programming is helpful.
Data Preprocessing In Data Mining
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Author : Salvador García
language : en
Publisher: Springer
Release Date : 2014-08-30
Data Preprocessing In Data Mining written by Salvador García and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-08-30 with Technology & Engineering categories.
Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data. This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying the techniques proposed in the specialized literature, is given.Each chapter is a stand-alone guide to a particular data preprocessing topic, from basic concepts and detailed descriptions of classical algorithms, to an incursion of an exhaustive catalog of recent developments. The in-depth technical descriptions make this book suitable for technical professionals, researchers, senior undergraduate and graduate students in data science, computer science and engineering.
Journal Of Biomimetics Biomaterials And Biomedical Engineering Vol 67
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Author : David Duday
language : en
Publisher: Trans Tech Publications Ltd
Release Date : 2025-01-15
Journal Of Biomimetics Biomaterials And Biomedical Engineering Vol 67 written by David Duday and has been published by Trans Tech Publications Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-15 with Medical categories.
The 67th volume of the journal comprises articles focused on biomimetic approaches in the design of quadruped robot and medical images, analysis of antibacterial and antimicrobial properties of some biomaterials and investigation of dolomite materials properties as a bioceramics substitute. The evaluation of tibia rotation in total knee arthroplasty designed for deep knee flexion using a knee kinematics motion simulator is also presented here. The presented research results will be useful to engineers in the area of robotics and biomedical engineering.