Machine Learning R Handbook
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Machine Learning R Handbook
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Author : Naresh R. Jasotani
language : en
Publisher: Naresh R. Jasotani
Release Date : 2020-06-10
Machine Learning R Handbook written by Naresh R. Jasotani and has been published by Naresh R. Jasotani this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-10 with Computers categories.
A machine learning handbook accompanied with series of video in a very precise and summarized format. This book is a summarized, a condensed handbook with videos for every section for you to learn easily. Course is oriented towards Data Science – No prerequisites. Primary goal is to give a kick-start to a Data Science Career. Follow the examples mentioned in the videos. Each topic is accompanied by Hands-on exercises. Deep focus on real-life time examples. Assuming no background experience – learn it at your own-pace. For every section you will find video links to understand the concepts better.
Machine Learning Essentials
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Author : Alboukadel Kassambara
language : en
Publisher: STHDA
Release Date : 2018-03-10
Machine Learning Essentials written by Alboukadel Kassambara and has been published by STHDA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-10 with Computers categories.
Discovering knowledge from big multivariate data, recorded every days, requires specialized machine learning techniques. This book presents an easy to use practical guide in R to compute the most popular machine learning methods for exploring real word data sets, as well as, for building predictive models. The main parts of the book include: A) Unsupervised learning methods, to explore and discover knowledge from a large multivariate data set using clustering and principal component methods. You will learn hierarchical clustering, k-means, principal component analysis and correspondence analysis methods. B) Regression analysis, to predict a quantitative outcome value using linear regression and non-linear regression strategies. C) Classification techniques, to predict a qualitative outcome value using logistic regression, discriminant analysis, naive bayes classifier and support vector machines. D) Advanced machine learning methods, to build robust regression and classification models using k-nearest neighbors methods, decision tree models, ensemble methods (bagging, random forest and boosting). E) Model selection methods, to select automatically the best combination of predictor variables for building an optimal predictive model. These include, best subsets selection methods, stepwise regression and penalized regression (ridge, lasso and elastic net regression models). We also present principal component-based regression methods, which are useful when the data contain multiple correlated predictor variables. F) Model validation and evaluation techniques for measuring the performance of a predictive model. G) Model diagnostics for detecting and fixing a potential problems in a predictive model. The book presents the basic principles of these tasks and provide many examples in R. This book offers solid guidance in data mining for students and researchers. Key features: - Covers machine learning algorithm and implementation - Key mathematical concepts are presented - Short, self-contained chapters with practical examples.
Machine Learning
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Author : Karen Mazidi
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2018-06-26
Machine Learning written by Karen Mazidi and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-26 with categories.
Machine Learning is a valuable skill for today's professionals. This book will give you the knowledge and skills you need to implement machine learning solutions. The approach is to give a conceptual understanding of the most common machine learning algorithms and practical experience in implementing them. Along the way, many practical tips are shared in the context of a wide array of machine learning examples. A companion website provides code samples used in the book. Download a free preview chapter at https: //karenmazidi.blogspot.com/
Machine Learning Handbook
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Author : Karen Mazidi
language : en
Publisher:
Release Date : 2020-12-03
Machine Learning Handbook written by Karen Mazidi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-03 with categories.
Machine Learning is a valuable skill for today's computing professionals. This handbook provides the guidance you need in building your skill set in this fast-growing field. R and Python programming are introduced in tutorials, and code samples are given throughout the book. In addition, all code samples are available in a GitHub repo, and a YouTube channel features explanatory video tutorials.
Machine Learning With R Quick Start Guide
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Author : Iván Pastor Sanz
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-03-29
Machine Learning With R Quick Start Guide written by Iván Pastor Sanz 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 2019-03-29 with Computers categories.
Learn how to use R to apply powerful machine learning methods and gain insight into real-world applications using clustering, logistic regressions, random forests, support vector machine, and more. Key FeaturesUse R 3.5 to implement real-world examples in machine learningImplement key machine learning algorithms to understand the working mechanism of smart modelsCreate end-to-end machine learning pipelines using modern libraries from the R ecosystemBook Description Machine Learning with R Quick Start Guide takes you on a data-driven journey that starts with the very basics of R and machine learning. It gradually builds upon core concepts so you can handle the varied complexities of data and understand each stage of the machine learning pipeline. From data collection to implementing Natural Language Processing (NLP), this book covers it all. You will implement key machine learning algorithms to understand how they are used to build smart models. You will cover tasks such as clustering, logistic regressions, random forests, support vector machines, and more. Furthermore, you will also look at more advanced aspects such as training neural networks and topic modeling. By the end of the book, you will be able to apply the concepts of machine learning, deal with data-related problems, and solve them using the powerful yet simple language that is R. What you will learnIntroduce yourself to the basics of machine learning with R 3.5Get to grips with R techniques for cleaning and preparing your data for analysis and visualize your resultsLearn to build predictive models with the help of various machine learning techniquesUse R to visualize data spread across multiple dimensions and extract useful featuresUse interactive data analysis with R to get insights into dataImplement supervised and unsupervised learning, and NLP using R librariesWho this book is for This book is for graduate students, aspiring data scientists, and data analysts who wish to enter the field of machine learning and are looking to implement machine learning techniques and methodologies from scratch using R 3.5. A working knowledge of the R programming language is expected.
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.
Machine Learning With R
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Author : Brett Lantz
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-04-15
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 2019-04-15 with Computers categories.
Solve real-world data problems with R and machine learning Key Features Third edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.6 and beyond Harness the power of R to build flexible, effective, and transparent machine learning models Learn quickly with a clear, hands-on guide by experienced machine learning teacher and practitioner, Brett Lantz Book Description Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings. This new 3rd edition updates the classic R data science book to R 3.6 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R. What you will learn Discover the origins of machine learning and how exactly a computer learns by example Prepare your data for machine learning work with the R programming language 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 — the basis of deep learning Avoid bias in machine learning models Evaluate your models and improve their performance Connect R to SQL databases and emerging big data technologies such as Spark, H2O, and TensorFlow Who this book is for Data scientists, students, and other practitioners who want a clear, accessible guide to machine learning with R.
Handbook Of Industrial Robotics
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Author : Shimon Y. Nof
language : en
Publisher: Krieger Publishing Company
Release Date : 1992
Handbook Of Industrial Robotics written by Shimon Y. Nof and has been published by Krieger Publishing Company this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Computers categories.
120 leading experts from twelve countries have participated in creating this Second Edition of the Handbook of Industrial Robotics. Of its 66 chapters, 33 are new, covering important new topics in the theory, design, control, and applications of robotics. Other key features include a larger glossary of robotics terminology with over 800 terms and a CD-ROM that vividly conveys the colorful motions and intelligence of robotics. With contributions from the most prominent names in robotics worldwide, the Handbook remains the essential resource on all aspects of this complex subject.
Machine Learning With R
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Author : Dominic Lordy
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2016-05-27
Machine Learning With R written by Dominic Lordy and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-05-27 with categories.
***** BUY NOW (Will soon return to 25.59) ******Free eBook for customers who purchase the print book from Amazon****** Are you thinking of learning more about Machine Learning using R? If you are looking for a complete beginners guide to learn Machine Learning using R, in just a few hours, this book is for you. Machine Learning is the practice of transforming data into knowledge, and R is the most popular open-source programming language used for Machine Learning. In this book, we will learn how to use the principles of Machine Learning and the R programming language to answer day-to-day questions about your data. Finally, we'll learn how to make predictions with machine learning. From AI Sciences Publisher Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. It will help you in preparing a solid foundation and learn any other high-level courses. To get the most out of the concepts that would be covered, readers are advised to adopt hands on approach, which would lead to better mental representations. Several Visual Illustrations and Examples Instead of tough math formulas, this book contains several graphs and images which detail all important R and Machine Learning concepts and their applications. Target Users The book designed for a variety of target audiences. The most suitable users would include: Beginners who want to approach Machine Learning, but are too afraid of complex math to start Newbies in computer science techniques and machine learning Professionals in Machine Learning and social sciences Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way Students and academicians, especially those focusing on Machine Learning What's Inside This Book? Introduction Basic Functions Linear Regression Machine Learning Algorithms Data with R Generating data Graphical functions Programming with R in Practice Opening the Black Box K-nearest Neighbors Neural Networks Trees and Forests Standard Linear Model Logistic Regression Support Vector Machine using R Frequently Asked Questions Help! I got an error, what did I do wrong? Useful References Frequently Asked Questions Q: Is this book for me and do I need programming experience? A: f you want to smash Machine Learning from scratch, this book is for you. Little programming experience is required. If you already wrote a few lines of code and recognize basic programming statements, you'll be OK. Q: Can I loan this book to friends? A: Yes. Under Amazon's Kindle Book Lending program, you can lend this book to friends and family for a duration of 14 days. Q: Does this book include everything I need to become a Machine Learning expert? A: Unfortunately, no. This book is designed for readers taking their first steps in Machine Learning and further learning will be required beyond this book to master all aspects of Machine Learning. Q: Can I have a refund if this book is not fitted for me? A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. We will also be happy to help you if you send us an email at [email protected]. If you need to see the quality of our job, AI Sciences Company offering you a free eBook in Machine Learning with Python written by the data scientist Alain Kaufmann at https: //aisciences.lpages.co/ai-sciences-data-science-with-r/
The Handbook Of Artificial Intelligence
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Author : Avron Barr
language : en
Publisher:
Release Date : 1982
The Handbook Of Artificial Intelligence written by Avron Barr and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1982 with Artificial intelligence categories.