Michael Walker - Data Cleaning and Exploration with Machine Learning: Get to grips with machine learning techniques to achieve sparkling-clean data quickly
Here you can read online Michael Walker - Data Cleaning and Exploration with Machine Learning: Get to grips with machine learning techniques to achieve sparkling-clean data quickly full text of the book (entire story) in english for free. Download pdf and epub, get meaning, cover and reviews about this ebook. year: 2022, publisher: Packt Publishing, genre: Home and family. Description of the work, (preface) as well as reviews are available. Best literature library LitArk.com created for fans of good reading and offers a wide selection of genres:
Romance novel
Science fiction
Adventure
Detective
Science
History
Home and family
Prose
Art
Politics
Computer
Non-fiction
Religion
Business
Children
Humor
Choose a favorite category and find really read worthwhile books. Enjoy immersion in the world of imagination, feel the emotions of the characters or learn something new for yourself, make an fascinating discovery.
- Book:Data Cleaning and Exploration with Machine Learning: Get to grips with machine learning techniques to achieve sparkling-clean data quickly
- Author:
- Publisher:Packt Publishing
- Genre:
- Year:2022
- Rating:3 / 5
- Favourites:Add to favourites
- Your mark:
Data Cleaning and Exploration with Machine Learning: Get to grips with machine learning techniques to achieve sparkling-clean data quickly: summary, description and annotation
We offer to read an annotation, description, summary or preface (depends on what the author of the book "Data Cleaning and Exploration with Machine Learning: Get to grips with machine learning techniques to achieve sparkling-clean data quickly" wrote himself). If you haven't found the necessary information about the book — write in the comments, we will try to find it.
Explore supercharged machine learning techniques to take care of your data laundry loads
Key Features- Learn how to prepare data for machine learning processes
- Understand which algorithms are based on prediction objectives and the properties of the data
- Explore how to interpret and evaluate the results from machine learning
Many individuals who know how to run machine learning algorithms do not have a good sense of the statistical assumptions they make and how to match the properties of the data to the algorithm for the best results.
As you start with this book, models are carefully chosen to help you grasp the underlying data, including in-feature importance and correlation, and the distribution of features and targets. The first two parts of the book introduce you to techniques for preparing data for ML algorithms, without being bashful about using some ML techniques for data cleaning, including anomaly detection and feature selection. The book then helps you apply that knowledge to a wide variety of ML tasks. Youll gain an understanding of popular supervised and unsupervised algorithms, how to prepare data for them, and how to evaluate them. Next, youll build models and understand the relationships in your data, as well as perform cleaning and exploration tasks with that data. Youll make quick progress in studying the distribution of variables, identifying anomalies, and examining bivariate relationships, as you focus more on the accuracy of predictions in this book.
By the end of this book, youll be able to deal with complex data problems using unsupervised ML algorithms like principal component analysis and k-means clustering.
What you will learn- Explore essential data cleaning and exploration techniques to be used before running the most popular machine learning algorithms
- Understand how to perform preprocessing and feature selection, and how to set up the data for testing and validation
- Model continuous targets with supervised learning algorithms
- Model binary and multiclass targets with supervised learning algorithms
- Execute clustering and dimension reduction with unsupervised learning algorithms
- Understand how to use regression trees to model a continuous target
This book is for professional data scientists, particularly those in the first few years of their career, or more experienced analysts who are relatively new to machine learning. Readers should have prior knowledge of concepts in statistics typically taught in an undergraduate introductory course as well as beginner-level experience in manipulating data programmatically.
Table of Contents- Examining the Distribution of Features and Targets
- Examining Bivariate and Multivariate Relationships between Features and Targets
- Identifying and Fixing Missing Values
- Encoding, Transforming, and Scaling Features
- Feature Selection
- Preparing for Model Evaluation
- Linear Regression Models
- Support Vector Regression
- K-Nearest Neighbor, Decision Tree, Random Forest and Gradient Boosted Regression
- Logistic Regression
- Decision Trees and Random Forest Classification
- K-Nearest Neighbors for Classification
- Support Vector Machine Classification
- Naive Bayes Classification
- Principal Component Analysis
- K-Means and DBSCAN Clustering
Michael Walker: author's other books
Who wrote Data Cleaning and Exploration with Machine Learning: Get to grips with machine learning techniques to achieve sparkling-clean data quickly? Find out the surname, the name of the author of the book and a list of all author's works by series.
Data Cleaning and Exploration with Machine Learning: Get to grips with machine learning techniques to achieve sparkling-clean data quickly — read online for free the complete book (whole text) full work
Below is the text of the book, divided by pages. System saving the place of the last page read, allows you to conveniently read the book "Data Cleaning and Exploration with Machine Learning: Get to grips with machine learning techniques to achieve sparkling-clean data quickly" online for free, without having to search again every time where you left off. Put a bookmark, and you can go to the page where you finished reading at any time.
Font size:
Interval:
Bookmark:
Get to grips with machine learning techniques to achieve sparkling-clean data quickly
Michael Walker
BIRMINGHAMMUMBAI
Copyright 2022 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Publishing Product Manager: Ali Abidi
Senior Editor: David Sugarman
Content Development Editor: Manikandan Kurup
Technical Editor: Rahul Limbachiya
Copy Editor: Safis Editing
Project Coordinator: Farheen Fathima
Proofreader: Safis Editing
Indexer: Hemangini Bari
Production Designer: Alishon Mendonca
Marketing Coordinators: Shifa Ansari and Abeer Riyaz Dawe
First published: August 2022
Production reference: 1290722
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham
B3 2PB, UK.
ISBN 978-1-80324-167-8
www.packt.com
Michael Walker has worked as a data analyst for over 30 years at a variety of educational institutions. He has also taught data science, research methods, statistics, and computer programming to undergraduates since 2006. He is currently the Chief Information Officer at College Unbound in Providence, Rhode Island.
Kalyana Bedhu is an engineering leader for data science at Microsoft. Kalyana has over 20 years of industry experience in data analytics across various companies such as Ericsson, Sony, Bosch Fidelity, and Oracle, among others. Kalyana was an early practitioner of data science at Ericsson, setting up a data science lab and building up competence in solving some practical data science problems. He played a pivotal role in transforming a central IT organization that dealt with most of the enterprise business intelligence, data, and analytical systems, into an AI and data science engine. Kalyana is a recipient of patents, a speaker, and has authored award-winning papers and data science courses.
Thanks to Packt and the author for the opportunity to review this book.
Divya Sardana serves as the lead AI/ ML engineer at Nike. Previously, she was a senior data scientist at Teradata Corp. She holds a Ph.D. in computer science from the University of Cincinnati, OH. She has experience working on end-to-end machine learning and deep learning problems involving techniques such as regression and classification. She has further experience in moving developed models to production and ensuring scalability. Her interests include solving complex big data and machine learning/deep learning problems in real-world domains. She is actively involved in the peer review of journals and books in the area of machine learning. She has served as a session chair at machine learning conferences such as ICMLA 2021 and BDA 2021.
Font size:
Interval:
Bookmark:
Similar books «Data Cleaning and Exploration with Machine Learning: Get to grips with machine learning techniques to achieve sparkling-clean data quickly»
Look at similar books to Data Cleaning and Exploration with Machine Learning: Get to grips with machine learning techniques to achieve sparkling-clean data quickly. We have selected literature similar in name and meaning in the hope of providing readers with more options to find new, interesting, not yet read works.
Discussion, reviews of the book Data Cleaning and Exploration with Machine Learning: Get to grips with machine learning techniques to achieve sparkling-clean data quickly and just readers' own opinions. Leave your comments, write what you think about the work, its meaning or the main characters. Specify what exactly you liked and what you didn't like, and why you think so.