Machine Learning with R - 6-week tutor-led online course


10/06/2022 - 15/07/2022

Organised by:

Mind Project Ltd


Simon Walkowiak MSc, MBPsS


Intermediate (some prior knowledge)


Simon Walkowiak
Phone: 02033223786

Venue: Online


1. Course description.

The powerful statistical capabilities of R programming language include a large selection of built-in methods and third-party libraries that contain an array of machine learning algorithms which can be applied for classification, clustering and predictive analytics. This hands-on "Machine Learning with R" course explores practical applications of the most frequently used machine learning approaches such a Multiple Linear, Polynomial (Non-Linear) and Logistic Regressions, k-Means and Hierarchical Clustering, k-Nearest Neighbours, Naive Bayes and Decision Trees algorithms through the R statistical environment. It also provides a good introduction to more advanced techniques e.g. Support Vector Machines, ensembles e.g. Random Forests, adaptive and gradient boosting techniques (AdaBoost, XGBoost etc.) and simple implementations of Artificial Neural Networks.

During the "Machine Learning with R" training course, your will be introduced to a variety of machine learning algorithms for classification and clustering, and their practical scenarios on real-word data using R language. Apart from this, you will learn to evaluate the predictive models based on the obtained classification metrics such as sensitivity, specificity, F-score, Kappa etc., and optimise the accuracy and efficiency of these models using various methods of cross-validation, grid-search and performance boosting.


2. Course programme.

This instructor-led course duration is planned over 6 teaching weeks.

In between the six weekly online live tutorials (2.5 hours long each) you will improve your skills by watching pre-recorded video tutorials at our Mind Project Learning Platform and working through set tasks (e.g. quizzes) as well as homework coding exercises which will require 4-6 hours of your time commitment per week (24-36 hours). We estimate that the total time commitment is 40-50 hours over 6 teaching weeks.

Start date: Friday, 10th of June 2022 @10:00 am London (UK) time

Schedule of sessions: Every Friday at 10:00 am London (UK) time for 6 weeks

Deadline for registrations: Wednesday, 8th of June 2022 @ 17:00 London (UK) time


Week 1: Introduction to Machine Learning with R with linear and non-linear regressions

  • Concepts, terminology and context: unsupervised vs. supervised vs. semi-supervised approaches,
  • Overview of methods and applications,
  • Preparing data for Machine Learning tasks: revision of probability distributions, data normalisation and standardisation techniques, feature engineering, dealing with missing values,
  • Multiple linear regression and selecting suitable predictors with stepwise regression,
  • Ridge and lasso regularisation,
  • Regression metrics for model evaluation, comparing models,
  • Polynomial regression, splines and generalised additive models (GAMs).


Week 2: Unsupervised learning with clustering approaches

  • K-means and k-medians clustering,
  • Hierarchical clustering,
  • Evaluating clustering solutions, describing clusters and estimating cluster profiles,
  • Overview of other important clustering methods: mean-shift, DBSCAN, Gaussian mixtures.


Week 3: Binary and multinomial classification - part 1: methods, evaluation metrics, model selection

  • Introduction to classification with logistic regression - understanding probabilities and log-odds,
  • Model selection and classification metrics: sensitivity, specificity, F score, Kappa, log-loss, R-squared etc.,
  • Linear and quadratic discriminant analysis,
  • Cross-validation and bootstrapping.


Week 4: Binary and multinomial classification - part 2: overview of other important approaches

  • Distance-based classification: k-Nearest Neighbours algorithm,
  • Probabilistic Naive Bayes classifier,
  • Kernel-based Support Vector Machines,
  • Semi-automated and automated tuning of classification models.


Weeks 5 & 6: From decision trees to ensembles and neural networks

  • Classification and Regression Trees (CART),
  • Estimating variable importance, bagging and boosting,
  • Tree-based Random Forests ensemble,
  • Adaptive Boosting (AdaBoost) and Extra Gradient Boosting (XGBoost) techniques,
  • Introduction to Artificial Neural Networks.


3. Course pre-requisites and further instructions

  • We recommend that you have the most recent version of R and R Studio software installed on your PC (any operating system). R is a free and open-source environment and you can download it directly from website. RStudio Desktop (also free) is available at Please contact us should you have any questions or issues with the installation process. A list of R packages to pre-install before the course will be sent to the enrolled attendees in the Welcome Pack alongside other Joining Instructions.
  • We recommend that the attendees have practical experience in data processing or quantitative research – gathered from either professional work or university education/research. A good knowledge of statistics would be beneficial. We suggest that the course is preceded with our “Applied Data Science with R” open-to-public tutor-led online training course.
  • Your PC needs to be connected to a stable WiFi/Internet network (either home or office-based) and have Zoom video-conferencing application installed.
  • You will need at least one commonly used web browser installed on your PC (e.g. Chrome, Safari, Firefox, Edge etc.) to access our Mind Project Learning Platform.

Should you have any questions please contact Mind Project Ltd at or by phone on 0203 322 3786. Please visit the course website at


By 13th of May 2022 (Early Bird offer): £345 (normally £420) per person for the whole course (regular fee). £225 (normally £270) per person for the whole course applicable to undergraduate and postgraduate students, representatives of registered charitable organisations and NHS employees only (discounted fee). Additional discounts available for multiple bookings and groups.

Website and registration:


Greater London


Statistical Theory and Methods of Inference, Bayesian methods, Regression Methods, Ordinary least squares (OLS), Generalized liner model (GLM), Linear regression, Logistic regression, Data Mining, Neural networks, Machine learning, Bootstrap simulation , R, Data Visualisation

Related publications and presentations:

Statistical Theory and Methods of Inference
Bayesian methods
Regression Methods
Ordinary least squares (OLS)
Generalized liner model (GLM)
Linear regression
Logistic regression
Data Mining
Neural networks
Machine learning
Bootstrap simulation
Data Visualisation

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