Data Science


Applied Machine Learning in R

Get the essential machine learning skills and use them in real life situations

4.50 (188 reviews)


8 hours


Dec 2020

Last Update
Regular Price

Exclusive  Offer
Unlimited access to 30 000 Premium SkillShare courses

What you will learn

Understand the essential concepts related to machine learning

Perform model cross-validation to assess model stability on independent data sets

Execute advanced regression analysis techniques: best subset selection regression, penalized regression, PLS regression

Perform logistic regression and discriminant analysis

Apply complex classification techniques: naive Bayes, K nearest neighbor, support vector machine, decision trees

Use neural networks to make predictions

Use principal components analysis to detect patterns in variables

Conduct cluster analysis to group observations into homogeneous classes


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 a solid expertise in machine learning.

Machine learning skills are very valuable if you intent to secure a job like data analyst, data scientist, researcher or even software engineer. So it may be the right time for you to enroll in this course and start building your machine learning competences 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, about the distinctions between prediction and inference, about the regression and classification models and, above all, about 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-validations 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 backward), penalized 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 neighbor, support vector machine, decision trees and neural networks.

For each technique above, the presentation is structured as follows:

* a short, easy to understand theoretical introduction (without complex mathematics)

* how to train the predictive model in R

* how to test the model to make sure that it does a good prediction job on independent data sets.

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 actually apply in practice what you have learned.

This course is your opportunity to become a machine learning expert in a few weeks only! 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 “Enroll” 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 in the near future.

See you inside!


Applied Machine Learning in R
Applied Machine Learning in R
Applied Machine Learning in R
Applied Machine Learning in R


Getting Started


Key Issues in Machine Learning

What Is Machine Learning?

Supervised vs. Unsupervised Methods

Prediction vs. Inference

Restrictive Models vs. Flexible Models

Computing Prediction Accuracy of Regression Models

Computing Prediction Accuracy of Classification Models

Bias-Variance Tradeoff


What Is Cross-Validation?

Validation Set Approach

Leave-One-Out Cross-Validation Approach

K-Fold Cross-Validation Approach

Ordinary Least Squares Regression

Introduction to the OLS Regression

Validating the OLS Regression Model (1)

Validating the OLS Regression Model (2)

Best Subset Regression

Best Subset Selection Regression - Introduction

Forward Selection Regression

Backward Selection Regression

Validating the Subset Selection Regression

Penalized Regression

Ridge Regression

Validating the Ridge Regression

Lasso Regression

Validating the Lasso Regression

Partial Least Squares Regression

Introduction to PLS Regression

Validating the PLS Regression

Logistic Regression

Introduction to Logistic Regression

Computing the Prediction Accuracy

Building the ROC Curve

Validating the Logistic Regression

Lasso Logistic Regression

Validating the Lasso Logistic Regression

Discriminant Analysis

Linear Discriminant Analysis

Validating the Linear Discriminant

Quadratic Discriminant Analysis

Validating the Quadratic Discriminant

Naive Bayes Estimation

Introduction to Naive Bayes Estimation

Naive Bayes Estimation in R with the e1071 Package

Validating the Naive Bayes Model

Naive Bayes Estimation in R with the naivebayes Package

K-Nearest Neighbor

Introduction to K Nearest Neighbor

K Nearest Neighbor in R

Finding the Optimal Number of Neighbors

Support Vector Machine

Maximal Margin Classifier

Support Vector Classifier

Introduction to Support Vector Machine

Support Vector Machine with Linear Kernel

Support Vector Machine with Polynomial Kernel

Support Vector Machine with Radial Kernel

Decision Trees (CART)

What Are Decision Trees?

Introduction to CART

Advantages and Disadvantages of Decision Trees

Growing Regression Trees in R

Growing Classification Trees in R

Introduction to Pruning

Pruning Regression Trees in R

Pruning Classification Trees in R

Introduction to Bagging and Random Forests

Bagging Regression Trees in R

Bagging Classification Trees in R

Random Forests of Regression Trees in R

Random Forests of Classification Trees in R

Introduction to Boosting

Boosting Regression Trees

Boosting Classification Trees

A Primer in Neural Networks

Definition of Neural Networks

What Happens Inside of a Neuron?

Neural Network Learning Process

A Simple Neural Network Example

Practical Neural Network Example

Practical Neural Network Example (2)

Principal Component Analysis

Introduction to Principal Component Analysis

Executing the Principal Component Analysis

Performing the Adequacy Tests

Cluster Analysis

Introduction to Cluster Analysis

Hierarchical Cluster

K-Means Cluster


Read Me First

Data Sets Descriptions

Practical Exercises

Download Links

Download Your Resources Here


Baris27 January 2021

So far so good. Nice started with good hopes and dynamism. I am very happy about taking this course...

Roger17 July 2020

Clear explanations. Keeping it simple to start with. Very succinct lectures. Well-annotated R code. Nice that PPT slides and R scripts are downloadable.

Jacky27 April 2020

I really like his teaching style. The course pace is so easy to follow. Everything is explaned very clear, and materials are sufficient for review. Thanks for creating this great course!!

Jose10 January 2019

The course gets you to the meat of the matter fast. Further details can be researched after you've learned the fundamentals. I believe some knowledge of statistical inference will be helpful to allow a more quicker consumption of the topics. This course can also serve as a quick revision for those with some background in ML already. In my opinion, Bogdan Anastasiei was able to structure the course in a manner that minimizes confusion (and maybe frustration) for the initiated-beginning ML learner.

Meghana7 December 2018

This course is like a ready reckoner for revising core concepts in a short duration of time. The concepts got cleared in one go. I would recommend this course to everyone who wants a crisp and a quick walk through of all the essentials in Machine Learning. Thank you Sir for creating this course!

Gulshan11 October 2018

I really like the approach where Mr. Bogdan explains the syntex as well as output. He makes it easier to comprehend the complex output as well how to tune the syntex. Eagerly looking for his promised course in deep learning and neural networks

Michael24 July 2018

This course provides a great balance between theory and its application in R for supervised and unsupervised machine learning techniques.

前田19 June 2018

This is a very informative course with clear explanation. If you want practical knowledge regarding machine learning with R. This is what you want.

Mohamed17 February 2018

The explanations are clear, and the course covers most of the main machine learning methods. The instructor is methodist and engaging. Looking forward for a complete "Neural Network" course. Keep up the good work.

Roberto19 January 2018

I had good experience with R but I still learned a lot on how to run a multitude of analyses with R. It's simple to understand and very practical in the use of R. My main struggle is that only in the last lecture it shows were to find the data for the lectures/exercise, it took me a while to figure it out. After that I was able to watch a lecture and at the same time practice with the same data frame but I didn't for the beginning of the course. The other limit is that there is almost no theoretical explanation on how to make a certain analysis and how to evaluate a model. If you already know the theory behind the material and only need to learn how to do these things in R, this is the class for you.

Amith2 January 2018

I am fan the instructor for his approach which is theoretical and practical. Since i have all his courses on R, It is easy for me to understand and implement while watching the course. Buy it for real time execution of the ML models. Thank you

Sameer30 December 2017

The course is very well structured. The course instructor is very clear in explaining the terms. This course is for those who would like to have a quick overview of Machine Learning techniques through R programming. I would strongly recommend this course. Also, I have enrolled myself in other courses of Prof. Anastasiei. Thank you very much Professor.

Kenneth26 December 2017

I enjoyed the course very much. it was my introduction into machine learning, and I am very satisfied with the course. I highly recommend this course.

Neel21 November 2017

I could learn many new facts about machine learning which would be useful for me in my day to day job life. However i think few techniques like lasso, regularisation could have been explained better and bit more detailed. I hope XGBOOST ML will be added to course contents in future. On overall, i am satisfied with the course.

Alexandre15 November 2017

Very good explanation, i ve already the knowledge but Bodgan explanation makes it clear. Hope he'll do other course in machine learning but with "deeper" technics.


7/20/2019100% OFFExpired
7/27/2020100% OFFExpired
11/27/202090% OFFExpired
6/10/2021100% OFFExpired


Udemy ID


Course created date


Course Indexed date
Course Submitted by