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From zero to hero - a journey into machine learning with R
Updated on Sep, 2026
Programming, Data Science, Machine Learning
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This course offers you practical training in machine learning, using the R program. At the end of the course, you will know how to use the most widespread machine-learning techniques to make accurate predictions and get valuable insights from your data.
All the machine learning procedures are explained live, in detail, on real-life data sets. So you will advance fast and be able to apply your knowledge immediately – no need for painful trial-and-error to figure out how to implement this or that technique in R. Within a short time, you can have solid expertise in machine learning.
Machine learning skills are very valuable if you intend to secure a job as a data analyst, data scientist, researcher or even software engineer. So it may be the right time for you to enrol in this course and start building your machine learning competencies today!
Let’s see what you are going to learn here.
First of all, we are going to discuss some essential concepts that you must absolutely know before performing machine learning. So we’ll talk about supervised and unsupervised machine learning techniques, the distinctions between prediction and inference, the regression and classification models and, above all, the bias-variance trade-off, a crucial issue in machine learning.
Next, we’ll learn about cross-validation. This is an all-important topic because, in machine learning, we must be able to test and validate our model on independent data sets (also called first-seen data). So we are going to present the advantages and disadvantages of three cross-validation approaches.
After the first two introductory sections, we will get to study the supervised machine learning techniques. We’ll start with the regression techniques, where the response variable is quantitative. And no, we are not going to stick to the classical OLS regression that you probably know already. We will study sophisticated regression techniques like stepwise regression (forward and backwards), penalised regression (ridge and lasso) and partial least squares regression. And of course, we’ll demonstrate all of them in R, using actual data sets.
Afterwards, we’ll go to the classification techniques, very useful when we have to predict a categorical variable. Here we’ll study the logistic regression (classical and lasso), discriminant analysis (linear and quadratic), naïve Bayes technique, K nearest neighbour, support vector machine, decision trees and neural networks. For each technique above, the presentation is structured as follows:
In the last sections, we’ll study two unsupervised machine learning techniques: principal component analysis and cluster analysis. They are powerful data mining techniques that allow you to detect patterns in your data or variables.
For each technique, a number of practical exercises are proposed. By doing these exercises, you’ll apply in practice what you have learned.
This course is your opportunity to become a machine learning expert in a few weeks! With my video lectures, you will find it very easy to master the major machine learning techniques. Everything is shown live, step by step, so you can replicate any procedure at any time you need it.
So click the “Enrol” button to get instant access to your machine learning course. It will surely provide you with new, priceless skills. And, who knows, it could give you a tremendous career boost shortly.
Check out the detailed breakdown of what’s inside the course
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