Machine Learning Made Easy With R

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Machine Learning Made Easy With R

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Author : N. Lewis
language : en
Publisher:
Release Date : 2017-05-07

Machine Learning Made Easy With R written by N. Lewis and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-07 with categories.

Finally, A Blueprint for Machine Learning with R! Machine Learning Made Easy with R offers a practical tutorial that uses hands-on examples to step through real-world applications using clear and practical case studies. Through this process it takes you on a gentle, fun and unhurried journey to creating machine learning models with R. Whether you are new to data science or a veteran, this book offers a powerful set of tools for quickly and easily gaining insight from your data using R. NO EXPERIENCE REQUIRED: This book uses plain language rather than a ton of equations; I'm assuming you never did like linear algebra, don't want to see things derived, dislike complicated computer code, and you're here because you want to try successful machine learning algorithms for yourself. YOUR PERSONAL BLUE PRINT: Through a simple to follow intuitive step by step process, you will learn how to use the most popular machine learning algorithms using R. Once you have mastered the process, it will be easy for you to translate your knowledge to assess your own data. THIS BOOK IS FOR YOU IF YOU WANT: Focus on explanations rather than mathematical derivation Practical illustrations that use real data. Illustrations to deepen your understanding. Worked examples in R you can easily follow and immediately implement. Ideas you can actually use and try on your own data. TAKE THE SHORTCUT: This guide was written for people just like you. Individuals who want to get up to speed as quickly as possible. to: YOU'LL LEARN HOW TO: Unleash the power of Decision Trees. Develop hands on skills using k-Nearest Neighbors. Design successful applications with Naive Bayes. Deploy Linear Discriminant Analysis. Explore Support Vector Machines. Master Linear and logistic regression. Create solutions with Random Forests. Solve complex problems with Boosting. Gain deep insights via K-Means clustering. Acquire tips to enhance model performance. For each machine learning algorithm, every step in the process is detailed, from preparing the data for analysis, to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks. Using plain language, this book offers a simple, intuitive, practical, non-mathematical, easy to follow guide to the most successful ideas, outstanding techniques and usable solutions available using R. Everything you need to get started is contained within this book. Machine Learning Made Easy with R is your very own hands on practical, tactical, easy to follow guide to mastery. Buy this book today and accelerate your progress!

Deep Learning Made Easy With R

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Author : N. D. Lewis
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2016-01-10

Deep Learning Made Easy With R written by N. D. Lewis 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 2016-01-10 with categories.

Master Deep Learning with this fun, practical, hands on guide. With the explosion of big data deep learning is now on the radar. Large companies such as Google, Microsoft, and Facebook have taken notice, and are actively growing in-house deep learning teams. Other large corporations are quickly building out their own teams. If you want to join the ranks of today's top data scientists take advantage of this valuable book. It will help you get started. It reveals how deep learning models work, and takes you under the hood with an easy to follow process showing you how to build them faster than you imagined possible using the powerful, free R predictive analytics package. Bestselling decision scientist Dr. N.D Lewis shows you the shortcut up the steep steps to the very top. It's easier than you think. Through a simple to follow process you will learn how to build the most successful deep learning models used for learning from data. Once you have mastered the process, it will be easy for you to translate your knowledge into your own powerful applications. If you want to accelerate your progress, discover the best in deep learning and act on what you have learned, this book is the place to get started. YOU'LL LEARN HOW TO: Understand Deep Neural Networks Use Autoencoders Unleash the power of Stacked Autoencoders Leverage the Restricted Boltzmann Machine Develop Recurrent Neural Networks Master Deep Belief Networks Everything you need to get started is contained within this book. It is your detailed, practical, tactical hands on guide - the ultimate cheat sheet for deep learning mastery. A book for everyone interested in machine learning, predictive analytic techniques, neural networks and decision science. Start building smarter models today using R! Buy the book today. Your next big breakthrough using deep learning is only a page away!

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.

R For Data Science

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Author : Hadley Wickham
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-12-12

R For Data Science written by Hadley Wickham and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-12 with Computers categories.

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

Deep Learning With R

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Author : François Chollet
language : en
Publisher: Simon and Schuster
Release Date : 2018-01-22

Deep Learning With R written by François Chollet and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-22 with Computers categories.

Summary Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples. Continue your journey into the world of deep learning with Deep Learning with R in Motion, a practical, hands-on video course available exclusively at Manning.com (www.manning.com/livevideo/deep-​learning-with-r-in-motion). Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. Deep-learning systems now enable previously impossible smart applications, revolutionizing image recognition and natural-language processing, and identifying complex patterns in data. The Keras deep-learning library provides data scientists and developers working in R a state-of-the-art toolset for tackling deep-learning tasks. About the Book Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. Initially written for Python as Deep Learning with Python by Keras creator and Google AI researcher François Chollet and adapted for R by RStudio founder J. J. Allaire, this book builds your understanding of deep learning through intuitive explanations and practical examples. You'll practice your new skills with R-based applications in computer vision, natural-language processing, and generative models. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image classification and generation Deep learning for text and sequences About the Reader You'll need intermediate R programming skills. No previous experience with machine learning or deep learning is assumed. About the Authors François Chollet is a deep-learning researcher at Google and the author of the Keras library. J.J. Allaire is the founder of RStudio and the author of the R interfaces to TensorFlow and Keras. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions

Machine Learning Made Easy

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Author : Timeo Williams
language : en
Publisher: Panel PR
Release Date : 2024-02-21

Machine Learning Made Easy written by Timeo Williams and has been published by Panel PR this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-21 with Computers categories.

Discover the power of machine learning with ease! Whether you're a beginner or seasoned pro, "Machine Learning Made Easy" is your go-to guide. From basics to real-world applications, this book breaks down complex concepts into simple, actionable steps. Learn core principles, practical techniques, and apply them to diverse fields like healthcare and finance. With clear explanations and hands-on examples, you'll master machine learning effortlessly. Don't miss out—unlock the potential of machine learning today!

Deep Learning Made Easy With R

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Author : N. D. Lewis
language : en
Publisher:
Release Date : 2016-05-07

Deep Learning Made Easy With R written by N. D. Lewis and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-05-07 with categories.

Who Else Wants to Master Deep Learning in Half the Time? Start building smarter models today using R ! Build Deep Learning Models Faster Then You Imagined Possible! This book provides an accessible, hands on, easy to follow guide to building deep learning models in R. If you want to accelerate your progress, discover the best in deep learning and act on what you have learned, this book is for you. It is designed for anyone who wishes to gain a practical understanding of the important modeling and prediction techniques that make up the increasingly lucrative discipline of deep learning. NO EXPERIENCE REQUIRED: - Bestselling data scientist Dr. N. D Lewis cuts a clear path through the jargon, opening the way for you to discover, understand, apply and exploit the potential of deep learning in your own research. Following on from the success of the first book in the Deep Learning Made Easy Series, it gives you new deep learning tools to use in your very own research. . YOU'LL LEARN HOW TO: Unleash the power of Kernel Deep Convex Neural Networks. Develop winning solutions with Deep Boosting. Explore, evaluate and exploit Monotone Neural Networks. Design successful solutions with Extreme Learning Machines. Master Deep Autoencoders. Ignite your use of Self-Organizing Polynomial Neural Networks. This hands on text is for individuals who want to master the subject in the minimum amount of time. It leverages the power of the FREE predictive analytic package R to provide you with the necessary tools to maximize your understanding, deepen your knowledge and unleash ideas to enhance your data science projects. THIS BOOK IS FOR YOU IF YOU WANT:: Real world applications that make sense. Examples to stimulate your thinking. Illustrations to deepen your understanding. Worked examples in R you can easily follow and immediately implement. Ideas you can actually use. Deep Learning Made Easy with R:Volume II is your very own hands on practical, tactical, easy to follow guide to mastery. Buy this book today and join the data science revolution!

Machine Learning Made Easy A Beginner S Guide For All

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Author : M.B. Chatfield
language : en
Publisher: M.B. Chatfield
Release Date :

Machine Learning Made Easy A Beginner S Guide For All written by M.B. Chatfield and has been published by M.B. Chatfield this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

Unleash the power of machine learning to automate tasks, make predictions, and solve complex problems. Machine learning is a powerful tool that can be used to automate tasks, make predictions, and solve complex problems. It is used in a wide variety of industries, including healthcare, finance, and manufacturing. Machine Learning Made Easy is the perfect resource for anyone who wants to learn the basics of machine learning. This comprehensive guide covers everything you need to know, from the basics of machine learning algorithms to advanced topics such as deep learning. Whether you're a student, a business professional, or a data enthusiast, Machine Learning Made Easy is the essential resource for learning about machine learning. Here are some of the key topics covered in the book: Introduction to machine learning Types of machine learning algorithms Choosing the right machine learning algorithm Training a machine learning model Evaluating a machine learning model Using machine learning to automate tasks Using machine learning to make predictions If you are a beginner who wants to learn about machine learning, Machine Learning Made Easy is a great place to start. #datascience #machinelearning #analyticsforeveryone #dataanalysisforbeginners #data #datavisualization #machinelearning #beginnersguide #learndata #GoogleAnalytics #Google #mobileapp #datavisualization #madeeasy #madesimple

Machine Learning With R The Tidyverse And Mlr

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Author : Hefin I. Rhys
language : en
Publisher: Manning
Release Date : 2020-03-31

Machine Learning With R The Tidyverse And Mlr written by Hefin I. Rhys and has been published by Manning this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-31 with Computers categories.

Summary Machine learning (ML) is a collection of programming techniques for discovering relationships in data. With ML algorithms, you can cluster and classify data for tasks like making recommendations or fraud detection and make predictions for sales trends, risk analysis, and other forecasts. Once the domain of academic data scientists, machine learning has become a mainstream business process, and tools like the easy-to-learn R programming language put high-quality data analysis in the hands of any programmer. Machine Learning with R, the tidyverse, and mlr teaches you widely used ML techniques and how to apply them to your own datasets using the R programming language and its powerful ecosystem of tools. This book will get you started! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the book Machine Learning with R, the tidyverse, and mlr gets you started in machine learning using R Studio and the awesome mlr machine learning package. This practical guide simplifies theory and avoids needlessly complicated statistics or math. All core ML techniques are clearly explained through graphics and easy-to-grasp examples. In each engaging chapter, you’ll put a new algorithm into action to solve a quirky predictive analysis problem, including Titanic survival odds, spam email filtering, and poisoned wine investigation. What's inside Using the tidyverse packages to process and plot your data Techniques for supervised and unsupervised learning Classification, regression, dimension reduction, and clustering algorithms Statistics primer to fill gaps in your knowledge About the reader For newcomers to machine learning with basic skills in R. About the author Hefin I. Rhys is a senior laboratory research scientist at the Francis Crick Institute. He runs his own YouTube channel of screencast tutorials for R and RStudio. Table of contents: PART 1 - INTRODUCTION 1.Introduction to machine learning 2. Tidying, manipulating, and plotting data with the tidyverse PART 2 - CLASSIFICATION 3. Classifying based on similarities with k-nearest neighbors 4. Classifying based on odds with logistic regression 5. Classifying by maximizing separation with discriminant analysis 6. Classifying with naive Bayes and support vector machines 7. Classifying with decision trees 8. Improving decision trees with random forests and boosting PART 3 - REGRESSION 9. Linear regression 10. Nonlinear regression with generalized additive models 11. Preventing overfitting with ridge regression, LASSO, and elastic net 12. Regression with kNN, random forest, and XGBoost PART 4 - DIMENSION REDUCTION 13. Maximizing variance with principal component analysis 14. Maximizing similarity with t-SNE and UMAP 15. Self-organizing maps and locally linear embedding PART 5 - CLUSTERING 16. Clustering by finding centers with k-means 17. Hierarchical clustering 18. Clustering based on density: DBSCAN and OPTICS 19. Clustering based on distributions with mixture modeling 20. Final notes and further reading