03528nam 2200433 450 991079344290332120190509070316.01-83864-705-8(CKB)4100000007878179(MiAaPQ)EBC5744469(CaSebORM)9781838644338(PPN)236074970(EXLCZ)99410000000787817920190428d2019 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierMachine learning with r quick start guide a beginner's guide to implementing machine learning techniques from scratch using r 3. 5 /Iván Pastor Sanz1st editionBirmingham ;Mumbai :Packt Publishing,2019.1 online resource (241 pages) illustrationsIncludes index.1-83864-433-4 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 Features Use R 3.5 to implement real-world examples in machine learning Implement key machine learning algorithms to understand the working mechanism of smart models Create end-to-end machine learning pipelines using modern libraries from the R ecosystem Book 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 learn Introduce yourself to the basics of machine learning with R 3.5 Get to grips with R techniques for cleaning and preparing your data for analysis and visualize your results Learn to build predictive models with the help of various machine learning techniques Use R to visualize data spread across multiple dimensions and extract useful features Use interactive data analysis with R to get insights into data Implement supervised and unsupervised learning, and NLP using R libraries Who 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.R (Computer program language)Machine learningR (Computer program language)Machine learning.519.502855133Sanz Iván Pastor1579810MiAaPQMiAaPQMiAaPQBOOK9910793442903321Machine learning with r quick start guide3860143UNINA