Data Science


Regression & Classification with Machine Learning in R

Learn Complete Hands-On Regression Analysis & Classification for applied Statistical Modelling and Machine Learning in R

5.00 (4 reviews)



4.5 hours


Feb 2021

Last Update
Regular Price

What you will learn

Your comprehensive guide to Regression Analysis & Classification for machine learning using R-programming language

It covers theory and applications of supervised machine learning with the focus on regression & classification analysis

Implement Machine Learning Techniques/Classification Such As Random Forests, SVM etc in R

Build machine learning based regression & classification models and test their robustness in R

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

Evaluate Model Performance & Learn The Best Practices For Evaluating Machine Learning Model Accuracy

Compare different different machine learning models in R

Learn R-programming from scratch: R crash course is included that you could start R-programming for machine learning

Graphically representing data in R before and after analysis


Regression Analysis and Classification for Machine Learning & Data Science in R

My course will be your hands-on guide to the theory and applications of supervised machine learning with a focus on regression analysis and classification 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 apply and understand REGRESSION ANALYSIS and CLASSIFICATION (Linear Regression, Random Forest, KNN, etc) in R. We will cover many R packages incl. caret package for supervised machine learning 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.


  • Fully understand the basics of supervised Machine Learning for Regression Analysis and classification tasks

  • Harness applications of parametric and non-parametric regressions & classification methods in R

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

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

  • Carry out coding exercises & your independent project assignment

  • Learn the basics of R-programming

  • Get a copy of all scripts used in the course

  • and MORE


You’ll start by absorbing the most valuable MAchine Learning & R-programming 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. Thus, after completing my Regression Analysis & Classification 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 & R-programming 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 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 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.



Regression & Classification with Machine Learning in R
Regression & Classification with Machine Learning in R
Regression & Classification with Machine Learning in R
Regression & Classification with Machine Learning in R




What is Machine Leraning and it's main types?

Machine Learning Types

Software used in this course R-Studio and Introduction to R

Introduction to Section 2

What is R and RStudio?

Lab: Install R and RStudio in 2020

Lab: Get started with R in RStudio

What is the current version of R and R-Studio

R Crash Course - get started with R-programming in R-Studio

Introduction to Section

Lab: Installing Packages and Package Management in R

Lab: Variables in R and assigning Variables in R

Overview of data types and data structures in R

Lab: data types and data structures in R

Vectors' operations in R

Dataframes: overview in R

Functions in R - overview

Read Data into R

Linear Regression in R

Introduction to Regression Analysis

Introduction to Regression Analysis

Graphical Analysis of Regression Models

Lab: your first linear regression model

Correlation in Regression Analysis in R: Lab

How to know if the model is best fit for your data - An overview

Linear Regression Diagnostics


Evaluation of Performance of Regression-based Prediction Model

Lab: Predict with linear regression model & RMSE as in-sample error

Prediction model evaluation with data split: out-of-sample RMSE

More types of regression models in R

Lab: Multiple linear regression - model estimation

Lab: Multiple linear regression - prediction

Nonlinear Regression Essentials in R: Polynomial and Spline Regression Models

Lab: Polynomial regression in R

Lab: Log transformation in R

Lab: Spline regression in R

Lab: Generalized additive models in R

Introduction to Model Selection Essentials in R

Supervised Machine Learning in R: Classification in R

Supervised Machine Learning & KNN: Overview

Overview of functionality of Caret R-package

Lab: Supervised classification with K Nearest Neighbours algorithm in R

Classification with the KNN-algorithm

Theory: Confusion Matrix

Lab: Calculating Classification Accuracy for logistic regression model

Compare the model accuracy (or any other metric) using thresholds of 0.1 and 0.9.

Lab: Receiver operating characteristic (ROC) curve and AUC

Working With Non-Parametric and Non-Linear Data (Supervised Machine Learning)

Classification and Decision Trees (CART): Theory

Lab: Decision Trees in R

Random Forest: Theory

Lab: Random Forest in R

Parametrise Random Forest model

Lab: Machine Learning Models' Comparison & Best Model Selection

Predict using the best model

Final Project Assignment



Anja9 February 2021

I really enjoyed the way you explained, and it's very easy for me to understand the concepts. The R course and practical labs are very helpful! Thank you so much!

Olha7 February 2021

Explanations and labs are very useful and are in simple language. Good content and material are provided in the course. overall a satisfactory experience achieved.

Stefanie4 February 2021

Machine learning was a totally new concept for me. I assumed that it would be very difficult to understand. This course helped me to be confident in mastering the concepts of Regression and Classification in R.


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