Machine Learning with R – London - 3-Day Training Course

Description:

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. The 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. Random Forests and simple implementations of Artificial Neural Networks. The course is suitable for data scientists, researchers, data analysts, developers and 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.

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.

2. Programme.

The course will run for three days (Wednesday to Friday) between 9:30am and 5:00pm and will consist of alternating lecture-style presentations and practical tutorials. The example datasets used during tutorial sessions will come from social sciences, psychology, business and finance fields, however the contents may vary depending on specific interests of participants (based on the Participant’s Skills Inventory). There will be two 15-minute coffee/tea breaks and one 1-hour lunch break on each day of the course.

The programme for this course covers the following concepts and topics:

• Predicting continuous target variables with different regression analysis techniques including multiple linear regressions, stepwise regressions, Lasso/Ridge regularised regressions, non-linear (polynomial) regressions and methods of their evaluation and optimisation,
• Understanding density functions and OLS normality assumption: screening for outliers, testing for normality (QQ-plots, histograms, Shapiro-Wilk and Kolmogorov-Smirnov tests), continuous data normalisation techniques, testing for multi-collinearity (creating correlation matrices, heatmaps etc.),
• Fitting polynomial regressions and regularisation approaches for polynomials (Lasso, Ridge, Elastic Net), searching for optimal lambda hyperparameter, overfitting vs underfitting,
• Applying k-means and hierarchical clustering algorithms for feature selection, dimensionality reduction and customer segmentation purposes,
• Implementing hierarchical clustering algorithm using different distance calculations and various linkage solutions; visualising clusters and understanding dendrograms, extracting segments and estimating cluster profiles,
• Implementing selected classification algorithms e.g. logistic regression and Naïve Bayes for binary and multinomial classification tasks,
• Choosing “best” models depending on obtained classification metrics e.g. confusion matrix, sensitivity, specificity, F score, Kappa statistic, logarithmic loss, R-squared, mean absolute error, root mean squared error, Gini score, area under ROC curve etc.,
• Feature engineering, cross-validation and grid-search methods for classification purposes,
• Applying more advanced classification and predictive analytics algorithms e.g. decision trees and their ensembles e.g. random forests and adaptive boosting in more complex machine learning applications.

3. What is included?

Apart from the contents of the course, Mind Project will provide you with the following:

• printed course pack with all presentation slides, cheatsheets and other essential course information,

• digital (USB memory stick) Course Manual including all presentation slides, R course codes and a list of reference books and online resources,

• additional home exercises and all data sets available to download,

• stimulating, friendly and inclusive learning environment in a small group (typically 10-14 attendees) led by experienced and energetic tutors and course leaders,

• modern and comfortable training venue located in the heart of City of London – at the London Institute of Banking & Finance, next to the Monument underground station,

• refreshments and a light, energising lunch on each day of the course,

• Wi-Fi access,

• networking opportunity,

• Mind Project course attendance certificate.

4. Further instructions.

• In order to benefit from the course, we recommended that all attendees have the most recent version of R and R Studio software installed on their personal laptops (any operating system). As R is a free and open-source environment you can download it directly from www.r-project.org website and R Studio is available at https://www.rstudio.com/products/rstudio/#Desktop. Please contact us should you have any questions or issues with the installation process. A list of specific R packages to install will be provided at least two weeks before the first day of the course.

• This course is targeted at R users with some R coding experience (preferably at Intermediate level) and interest in Machine Learning algorithms. Our “Applied Data Science with R” training course (details at https://www.mindproject.io/product/applied-data-science-with-r-london-may-2019/) is a good pre-requisite to participate in this course.

• The deadline for registrations on this training course is Friday, 31st of May 2019 at 16:00 London (UK) time. Mind Project reserves the right to end the registration process earlier if all places are booked before the deadline.

Should you have any questions please contact Mind Project Ltd at info@mindproject.co.uk or by phone on 0203 322 3786. Please visit the course website at https://www.mindproject.io/product/machine-learning-with-r-london-june-2019/.

Cost:

£825 per person for the whole course (regular fee).
£675 per person for the whole course for UK registered undergraduate and postgraduate students, and representatives of registered charitable organisations (discounted fee).
For group bookings of 4 and more delegates, please contact us directly.

Website and registration:

https://www.mindproject.io/product/machine-learning-with-r-london-june-2019/

Region:

Greater London

Keywords:

Statistical Theory and Methods of Inference, Probability theory , Bayesian methods, Regression Methods, Ordinary least squares (OLS), Generalized liner model (GLM), Linear regression, Logistic regression, Forecasting, Data Mining, Machine learning, Quantitative Software, R

Related publications and presentations:

Statistical Theory and Methods of Inference
Probability theory
Bayesian methods
Regression Methods
Ordinary least squares (OLS)
Generalized liner model (GLM)
Linear regression
Logistic regression
Forecasting
Data Mining
Machine learning
Quantitative Software
R

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