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Harness Power of R for unsupervised machine Learning (k-means, hierarchical clustering) – With Practical Examples in R
Description
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
– Fully understand the basics of Machine Learning, Cluster Analysis & Unsupervised Machine Learning from theory to practice
– Harness applications of unsupervised learning (cluster analysis) in R and with Google Cloud Services
– Machine Learning, Supervised Learning, Unsupervised Learning in R
– Complete two independent projects on Unsupervised Machine Learning in R and using Google Cloud Services
– Implement Unsupervised Clustering Techniques (k-means Clustering and Hierarchical Clustering etc)
– and MORE
NO PRIOR R OR STATISTICS/MACHINE LEARNING / R KNOWLEDGE REQUIRED:
You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R.
My course will help you implement the methods using real data obtained from different sources, including implementing a real-life project on the cloud computing platform of Google. Thus, after completing my unsupervised data clustering course in R, you’ll easily use different data streams and data science packages to work with real data in R.
I will also provide you with the all scripts and data used in the course.
In case it is your first encounter with R, don’t worry, my course a full introduction to the R & R-programming in this course.
This course is different from other training resources. Each lecture seeks to enhance your data science and clustering skills (K-means, Hierarchical clustering, weighted-K means, Heat mapping, etc) in a demonstrable and easy-to-follow manner and provide you with practically implementable solutions. You’ll be able to start analyzing different streams of data for your projects and gain appreciation from your future employers with your improved machine learning skills and knowledge of the cutting edge data science methods.
The course is ideal for professionals who need to use cluster analysis, unsupervised machine learning, and R in their field.
One important part of the course is the practical exercises. You will be given some precise instructions and datasets to run Machine Learning algorithms using the R and Google Cloud Computing tools.
JOIN MY COURSE NOW!
Who this course is for:
The course is ideal for professionals who need to use cluster analysis, unsupervised machine learning and R in their field.
Everyone who would like to learn Data Science Applications In The R & R Studio Environment
Everyone who would like to learn theory and implementation of Unsupervised Learning On Real-World Data
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