Course summary

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.).

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.

A mixture of online pre-recorded instruction videos, weekly live group webinars with our tutor, additional 1-2-1 check-ins (either via email or on Microsoft Teams) and several homework exercises throughout the duration of the course will ensure you will be able to apply Machine Learning methods using R language to your own data and research questions.

Who is this course for?

This tutor-led online course is suitable for data scientists, researchers, Master’s or PhD students, post-doctoral researchers, data analysts, developers and data engineers, who are currently using R language (preferably at intermediate level) and would like to expand their skills to include machine learning and predictive analytics toolkit.

What is included in the course fee?

At Mind Project, we pride ourselves for delivering great quality, goal-directed, interactive and cost-effective training. Our remote, hybrid, and on-premises tutor-led courses are designed to maximise your opportunity to learn complex topics in shortest possible time while being able to discuss and interact with our tutor and other fellow attendees. Therefore, the registration fee for this course includes a number of benefits that many other training providers don’t offer:

• Weekly (90-minute long) remotely delivered live group webinars with our expert tutor over the period of 6 weeks – you will be able to ask questions, interact and discuss the topic with other attendees from the comfort of your home/office,
• you will have access to course online materials for 1 year via our Mind Project Learning Platform e.g. tutorial videos, exercises and quizzes, R code scripts used during tutorials and live sessions, example datasets, optional and mandatory reading (e.g. blog articles, academic papers, industry reports), and other external recommended resources e.g. online books,
• 2-week post-course follow-up period during which you will be able to work on homework exercises and submit them for our feedback – the feedback will be provided either via email or Microsoft Teams (i.e. as a 15-minute 1-2-1 session with our tutor),
• exercises and tasks completed by you between the live webinar sessions will help you in better material retention and will enhance your learning progress,
• email supervision and weekly check-ins during the course period will maximise your learning outcomes and improve your learning experience; we will provide you with support throughout the course and the 2-week post-course follow-up period – feel free to ask us questions about the material presented at the training during this period,
• small group size allows easy interaction and stimulating environment for successful learning.

Programme outline

During this course, you will learn and implement a variety of statistical and machine learning methods including common classifiers and clustering approaches as well as more advanced predictive analytics models such as ensembles and supermodels. The course will be run according to the following schedule:

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, and DBSCAN.

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

• 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.