HERE IS WHY YOU SHOULD TAKE THIS COURSE:
This course will be your complete guide to unsupervised learning and clustering using R–programming language and JavaScript.
Unlike other courses, it offers NOT ONLY the guided demonstrations of the R–scripts but also covers theoretical background that will allow you to FULLY UNDERSTAND & APPLY UNSUPERVISED MACHINE LEARNING (K–means, Hierarchical clustering) in R.
This course also covers all the main aspects of practical and highly applied data science related to unsupervised machine learning and clustering techniques. Thus, if you take this course, you will save lots of time & money on other expensive materials in the R based data science domain.
In this age of big data, companies across the globe use R and Google Cloud Computing Services to analyze big volumes of data for business and research. By becoming proficient in unsupervised learning in R, you can give your company a competitive edge and boost your career to the next level. In addition, you will have a chance to test the power of cloud computing with Google services (i.e. Earth Engine) for a real–world application of unsupervised K–means learning for mapping applications.
THIS COURSE HAS 8 SECTIONS COVERING EVERY ASPECT OF UNSUPERVISED MACHINE LEARNING: THEORY & PRACTISE
Specification: Cluster Analysis & Unsupervised Machine Learning in R
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5 reviews for Cluster Analysis & Unsupervised Machine Learning in R
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Price | $9.99 |
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Provider | |
Duration | 4.5 hours |
Year | 2021 |
Level | All |
Language | English ... |
Certificate | Yes |
Quizzes | No |
$19.99 $9.99
Stefanie Huber –
Great learning! Enjoyed watching this course. Thanks a lot Kate for your passion in teaching the content
Olha Zaharchuk –
So far this course is much better than the professor who taught us at university! Very useful practical labs!
Anna Brot –
This is exactly what I was looking for, and just what I wanted to learn. Thank you!
JIMOH Olalekan Abeeb –
Yes. It is very good
John Alexander Sandoval Barrera –
Sometimes the R scripts used to give examples in the lectures do not correspond with the ones that are available to downloads and do not run.