Packt – Clustering and Classification with Machine Learning in R-XQZT

Packt – Clustering and Classification with Machine Learning in R-XQZT
English | Size: 1.34 GB
Category: Tutorial

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.

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