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Machine Learning in R & Predictive Models |Theory & Practice

Machine Learning in R & Predictive Models |Theory & Practice

Free 19.99
Expired
Supervised & unsupervised machine learning in R, clustering in R, predictive models in R by many labs, understand theory
New
Rating: 4.4 out of 5
 (15 ratings)
5,596 students
Created by Kate AlisonGeorg Müller
Last updated 1/2021
English

Requirements

  • Availability computer and internet & strong interest in the topic

Description

Machine Learning in R & Predictive Models |Theory & Practice

My course will be your complete guide to the theory and applications of supervised & unsupervised machine learning and predictive modeling using the R-programming language.

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 MACHINE LEARNING & PREDICTIVE MODELS (K-means, Random Forest, SVM, logistic regression, etc) in R (many R packages incl. caret package will be covered).

This course also covers all themain aspects of practical and highly applied data science related to Machine Learning (classification & regressions) and unsupervised 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 and Machine Learning domain.

In this age of big data, companies across the globe use R to analyze big volumes of data for business and research. By becoming proficient in supervised & unsupervised machine learning and predictive modeling in R, you can give your company a competitive edge and boost your career to the next level

THIS COURSE HAS 8 SECTIONS COVERING EVERY ASPECT OF MACHINE LEARNING: BOTH THEORY & PRACTISE

  • Fully understand the basics of Machine Learning, Cluster Analysis & Prediction Models from theory to practice
  • Harness applications of supervised machine learning (classification and regressions) and Unsupervised machine learning (cluster analysis) in R
  • Learn how to apply correctly prediction models and test them in R
  • Complete programming & data science tasks in an independent project on Supervised Machine Learning in R
  • Implement Unsupervised Clustering Techniques (k-means Clustering and Hierarchical Clustering etc)
  • Learn the basics of R-programming
  • Get a copy of all scripts used in the course
  • and MORE

NO PRIOR R OR STATISTICS/MACHINE LEARNING / R KNOWLEDGE REQUIRED:

You’ll start by absorbing the most valuable R Machine Learning, Predictive Modelling & 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 youimplement the methods using real dataobtained from different sources. Thus, after completing my Machine Learning course in R, you’ll easily use different data streams and data science packages to work with real data in R.

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 Machine Learning and modeling skills 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 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 Machine Learning On Real-World Data
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