Machine Learning with R - London - 2-Day Training Course

Description:

1. Course description.

The powerful statistical capabilities of R programming language include a large selection of machine learning algorithms which can be applied to classification, clustering and predictive analytics. This course explores practical applications of the most frequently used machine learning methods such a k-Nearest Neighbours, Naive Bayes, Regressions and Decision Trees algorithms through R statistical environment. It also provides a good introduction to more advanced techniques e.g. Artificial Neural Networks and Support Vector Machines. The course aims to achieve the following goals:

• to introduce attendees to a variety of machine learning algorithms for classification and forecasting, and their practical applications through R language,
• to present numerous third-party packages which facilitate ML application in R,
• to guide the attendees through a range of R functions specific to each machine learning algorithm and implement them in practical scenarios with real-world data,
• to explain the importance of Machine Learning model performance and ways of its evaluation and improvement,
• to implement selected Machine Learning algorithms using R and H2O framework.

The course will be presented by Simon Walkowiak - an author of "Big Data Analytics with R" (used as a textbook at the University of Oxford "Data Science for the Internet of Things" course) and Mind Project's consultant in Big Data architecture for predictive modelling. Simon has delivered numerous "Big Data Methods in R" training courses at various institutions, financial/business organisations, governmental departments and UK universities (including Big Data & Analytics Summer School organised by the Institute for Analytics and Data Science). He is also a former Data Curator at the UK Data Archive - the largest socio-economic digital data depository in Europe.

2. Programme.

The course will run for two days from 9:30am until ~5:00pm on each day and will consist of alternating lecture-style presentations and practical tutorials. The example datasets used during tutorial sessions will come from social sciences, economics and business 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.

3. What is included?

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

• a 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 and code scripts available to download,
• Wi-Fi access,
• Central London location - at the CAP House, next to the Barbican station,
• networking opportunity,
• Mind Project course attendance certificate.

4. Further instructions.

• In order to benefit from the contents of the course it is recommended that 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 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. You will also be provided with a list of specific R packages to download and install before the course.

• This course is targeted at users with some R experience and interest in Machine Learning algorithms. Our “Applied Data Science in R” training course is a good pre-requisite to participate in this course.

• Participants are encouraged to complete the online Participant's Skills Inventory at https://www.mindproject.io/participants-skills-inventory/ to allow Mind Project and our course tutors to customise the contents of the course depending on the level of participants' knowledge and their areas of interest. The data obtained through the Participant's Skills Inventory will be held fully-confidential and will only be used to provide a quality data analysis training.

• By purchasing a place on one of our courses you agree to the Terms and Conditions. Please read the Terms and Conditions available at https://www.mindproject.io/services/data-science-training/training-tcs/ before making a booking.

• The deadline for registrations on this training course is Tuesday, 10th of October 2017 at 18:00 London (UK) time, however 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-october-2017/. 

Cost:

£450 per person for the whole course (regular fee).
£300 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 participants, please contact us directly.

Website and registration:

https://www.mindproject.io/product/machine-learning-with-r-london-october-2017/

Region:

Greater London

Keywords:

Statistical Theory and Methods of Inference, Bayesian methods, Regression Methods, Ordinary least squares (OLS), Generalized liner model (GLM), ANOVA, Linear regression, Logistic regression, Poisson regression, Data Mining, Neural networks, Machine learning, Quantitative Approaches (other), Quantitative Software, R

Related publications and presentations:

Statistical Theory and Methods of Inference
Bayesian methods
Regression Methods
Ordinary least squares (OLS)
Generalized liner model (GLM)
ANOVA
Linear regression
Logistic regression
Poisson regression
Data Mining
Neural networks
Machine learning
Quantitative Approaches (other)
Quantitative Software
R

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