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Clustering and Classification with Machine Learning in R
Clustering and Classification with Machine Learning in R

Clustering and Classification with Machine Learning in R: The underlying patterns in your data hold vital insights; unearth them with cutting-edge clustering and classification techniques in R

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Profile Icon Minerva Singh
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Video Nov 2019 7hrs 42mins 1st Edition
Video
zł39.99 zł725.99
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Free Trial
Arrow left icon
Profile Icon Minerva Singh
Arrow right icon
Free Trial
Video Nov 2019 7hrs 42mins 1st Edition
Video
zł39.99 zł725.99
Subscription
Free Trial
Video
zł39.99 zł725.99
Subscription
Free Trial

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Key benefits

  • Provides in-depth training in everything you need to know to get started with practical R data science
  • Jargon-free and suitable for people who have a non-mathematical background
  • In-depth coverage of the latest unsupervised and supervised techniques

Description

This course is your complete guide to both supervised and unsupervised learning using R. This course covers all the main aspects of practical data science; if you take this course, there is no need to take other courses or buy books on R-based data science. In this age of big data, companies across the Globe use R to sift through the avalanche of information at their disposal. By becoming proficient in unsupervised and supervised learning in R, you can give your company a competitive edge and take your career to the next level. Over the course of research, the author realized that almost all the R data science courses and books out there do take account of the multidimensional nature of the topic. This course will give you a robust grounding in the main aspects of machine learning: clustering and classification. Unlike other R instructors, the author digs deep into R's machine learning features and give you a one-of-a-kind grounding in data science! You will go all the way from carrying out data reading & cleaning to machine learning, to finally implementing powerful machine learning algorithms and evaluating their performance via R. The following topics will be covered: - • A full introduction to the R Framework for data science • Data structures and reading in R, including CSV, Excel, and HTML data • How to pre-process and clean data by removing NAs/No data, visualization • Machine learning, supervised learning, and unsupervised learning in R • Model building and selection and much more! The course will help you implement methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R-based data science in real life. After taking this course, you'll easily use data science packages such as Caret to work with real data in R. You'll even understand concepts such as unsupervised learning, dimension reduction, and supervised learning. All the code and supporting files for this course are available at - https://github.com/PacktPublishing/Clustering-and-Classification-with-Machine-Learning-in-R

Who is this book for?

This course is for students interested in getting started with data science applications in the R Studio environment. Students wishing to learn how to implement unsupervised learning on real data. Anyone with prior exposure to R who wants to get started with practical data science.

What you will learn

  • Read-in data into the R environment from different sources
  • Carry out basic data pre-processing and wrangling in R Studio
  • Implement unsupervised/clustering techniques such as K-means clustering
  • Implement dimensional reduction techniques (PCA) and feature selection
  • Implement supervised learning techniques/classification such as Random Forests
  • Evaluate model performance and learn the best practices for evaluating machine learning model accuracy

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Nov 28, 2019
Length: 7hrs 42mins
Edition : 1st
Language : English
ISBN-13 : 9781838984571
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Product Details

Publication date : Nov 28, 2019
Length: 7hrs 42mins
Edition : 1st
Language : English
ISBN-13 : 9781838984571
Category :
Languages :
Tools :

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Table of Contents

10 Chapters
Introduction to the Course Chevron down icon Chevron up icon
Read in Data from Different Sources in R Chevron down icon Chevron up icon
Data Pre-processing and Visualization Chevron down icon Chevron up icon
Machine Learning for Data Science Chevron down icon Chevron up icon
Unsupervised Learning in R Chevron down icon Chevron up icon
Feature/Dimension Reduction Chevron down icon Chevron up icon
Feature Selection to Select the Most Relevant Predictors Chevron down icon Chevron up icon
Supervised Learning Theory Chevron down icon Chevron up icon
Supervised Learning: Classification Chevron down icon Chevron up icon
Additional Lectures Chevron down icon Chevron up icon
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