Description
What you’ll learn

Your comprehensive guide to Regression Analysis & supervised machine learning using Rprogramming language

Graphically representing data in R before and after analysis

It covers the theory and applications of supervised machine learning with the focus on regression analysis using the Rprogramming language in RStudio

Implement Ordinary Least Square (or simple linear) regression, Random FOrest Regression, Decision Trees, Logistic regression and others using R

Perform model’s variable selection and assess regression model’s accuracy

Build machine learning based regression models and test their performance in R

Compare different different machine learning models for regression tasks in R

Learn how to select the best statistical & machine learning model for your task

Learn when and how machine learning models should be applied

Carry out coding exercises & your independent project assignment
Regression Analysis for Machine Learning & Data Science in R
My course will be your handson guide to the theory and applications of supervised machine learning with the focus on regression analysis using the Rprogramming language.
Unlike other courses, it offers NOT ONLY the guided demonstrations of the Rscripts but also covers theoretical background that will allow you to FULLY UNDERSTAND & APPLY REGRESSION ANALYSIS (Linear Regression, Random Forest, KNN, etc) in R (many R packages incl. caret package will be covered) for supervised machine learning and prediction tasks.
This course also covers all the main aspects of practical and highly applied data science related to Machine Learning (i.e. regression analysis). 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.
THIS COURSE HAS 8 SECTIONS COVERING EVERY ASPECT OF MACHINE LEARNING: BOTH THEORY & PRACTISE

Fully understand the basics of Regression Analysis (parametric & nonparametric methods) & supervised Machine Learning from theory to practice

Harness applications of parametric and nonparametric regressions in R

Learn how to apply correctly regression models and test them in R

Learn how to select the best statistical & machine learning model for your task

Carry out coding exercises & your independent project assignment

Learn the basics of Rprogramming

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 Regression Analysis & Rprogramming basics, and techniques. I use easytounderstand, handson 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. Thus, after completing my Regression Analysis for Machine Learning in R course, 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 & Rprogramming in this course.
This course is different from other training resources. Each lecture seeks to enhance your Regression modeling and Machine Learning skills in a demonstrable and easytofollow 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 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 regression analysis & supervised machine learning 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 Regression Analysis & Machine Learning On RealWorld Data
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