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.
Please note this training course doesn’t include Deep Learning approaches – our “Deep Learning with R” course is specifically designed to cover these methods in detail.
Who is this course for?
This tutor-led online course is suitable for data scientists, researchers, Master’s or PhD students, data analysts, developers and engineers, who are currently using Python language (preferably at intermediate level) and would like to expand their skills to include machine learning and predictive analytics toolkit. This course is especially recommended for undergraduate and postgraduate students in social sciences, computer science, data science, economy, finance/banking, public health and medical science, and all other fields which implement machine and statistical learning methods.
This training course is tutor-led – all online tutorials are presented live by our expert instructor, you can ask questions, discuss the topic and interact with other learners,
In total, this course includes 15 hours of live teaching which equates to a 3-day full-time classroom-based training course with exclusion of tea/coffee and lunch breaks,
Exercises and tasks completed by you between the live tutorial sessions will help you in better material retention and will enhance your learning progress,
Small group size (up to 15 learners) allows easy interaction and stimulating environment for successful learning,
Email supervision and support provided during the course period maximises learning outcomes and improves your learning experience,
You will have access to additional self-paced online learning materials e.g. tutorial videos, exercises and quizzes, R code scripts used during tutorials, example datasets, optional and mandatory reading (e.g. blog articles, academic papers, industry reports), and other external recommended resources e.g. online books,
You will receive a course completion certificate upon successful submission of R coding exercises and quizzes,
You will be encouraged to network with other learners enrolled on the course – you will automatically become a member of the course forum/message board where you can ask general course questions and you will maintain your learner’s profile in which you can share your bio and social media links.
Your course instructor
Your instructor for this course will be Simon Walkowiak. Simon is a director at Mind Project Limited and a Ph.D. researcher in Artificial Intelligence at the Bartlett Centre for Advanced Spatial Analysis (University College London) and the Alan Turing Institute in London. Simon holds BSc (First Class Honours) in Psychology with Neuroscience and MSc (Distinction) in Big Data Science. He conducts and manages research projects on implementation and computational optimisation of novel AI approaches applicable to large-scale datasets to predict human behaviour and spatial cognition. Simon is the author of “Big Data Analytics with R” (2016) – a widely used textbook on high-performance computing with R language and its compatibility with ecosystem of Big Data tools e.g. SQL/NoSQL databases, Spark, Hadoop etc. Apart from research and data management consultancy, during the past several years, Simon has taught at more than 150 in-house or open-to-public statistical training courses (in R, Python, SQL and Scala for Spark languages) in the UK, Europe, Asia and USA. His major clients include organisations from finance and banking (HSBC, RBS, GE Capital, European Central Bank, Credit Suisse etc.), research and academia (GSMA, CERN, UK Data Archive, Agri-Food Biosciences Institute, Newcastle University etc.), health (NHS), and government (Home Office, Ministry of Justice, Government Actuary’s Department etc.).
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: Tuesday, 4th of May 2021 @14:30 London (UK) time
Schedule of sessions: Every Tuesday at 14:30 London (UK) time for 6 weeks
Deadline for registrations: Friday, 30th of May 2021 @ 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.
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 https://cloud.r-project.org/ website. RStudio Desktop (also free) is available at https://rstudio.com/products/rstudio/download/. 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.
The primary spoken and written language of the course is English.
You are encouraged to complete the online Learner’s Skills Inventory to allow Mind Project and our course tutors to customise the course teaching style depending on the level of attendees’ knowledge and their areas of interest. The data obtained through the Learner’s Skills Inventory will be held fully-confidential and will only be used to provide a quality statistical computing and data science training.
Discounts and multiple bookings
Early Bird Offer allows you to save up to 30% off the total course enrolment price (on top of other discounts). This offer is usually valid up until 3-4 weeks before the start of the course.
We offer 2 types of enrolment options:
- Regular Fee – full-priced enrolment for learners representing commercial organisations or self-funded individuals who do not meet our eligibility criteria for discounted rates (please see below),
- Discounted Fee – applicable to undergraduate and postgraduate students as well as representatives of registered charitable organisations and non-governmental organisations (NGOs) – this category also includes employees of the National Health Service (NHS).
Students and individuals eligible for the Discounted Fee should submit a copy of their student or organisation ID card (with their name and card expiry date visible) when making the purchase of their place on the course for the discount eligibility verification purposes. Alternatively, the discount eligibility can be verified by submitting either i.) a copy of a letter from the university registrar or student’s department confirming your status, or ii.) a copy of a letter from your employer (on a company letter-headed paper with a charity/NGO registration number) which confirms your current position within the organisation.
Apart from the Early Bird Offer and discounted fees for students or employees of charitable organisations and NGOs, we are able to offer further discounts on the overall cost of your training if you wish to attend multiple related courses or enrol several delegates on this specific course. Please note that this offer is only available through our website.
- If you book 3 or 4 tickets on any of our tutor-led open-to-public online training courses, you will receive 5% discount on the total price of your booking.
- If you book 5 or more tickets on any of our tutor-led open-to-public online training courses, you will receive 10% discount on the total price of your booking.
All discounts are calculated automatically when tickets are added to the Cart. For bookings of 6 and more delegates on one course, we recommend that you contact us directly – we may be able to arrange a separate course just for your delegates at a discounted rate.
Arrange this course at your organisation
This open-to-public online course is a more generalised version of our fully-customisable in-house / online training course “Machine Learning with Python”. If your delegates cannot attend this public course, or you are interested in arranging this training course explicitly for your delegates (or at your premises) or simply you need a bespoke, made-to-measure training solution, please request a quote for the in-house version of this course based on your specific needs and desired outcomes of the training.
You may email us directly at info(at)mindproject.io and include the following information in your enquiry:
contact details to a person who should receive the quote,
number of delegates you would like to train,
approximate number of online sessions (or half-days / full days for on-site in-house course) you would like to arrange the course for (including additional support/project guidance if needed),
location of the training venue if not online,
any details on course customisation or specific topics you would like the course to address – most importantly, please indicate desired outcomes of the course if different then presented above,
any other questions you may have.
If you don’t know the answers to questions above or you are at early stages of course planning, we would be happy to arrange an informal chat and help you choose the most suitable and budget-efficient option.