Machine Learning with R
Machine Learning with R - London - March 2017

Machine Learning with R – London – March 2017

£300.00£450.00

When? Thursday – Friday, 9th – 10th of March 2017, 9:30 am – 5:00 pm

Where? CAP House, 1st Floor, 9-12 Long Lane, London, EC1A 9HA

Course type? Specialist, two-day training course.

Deadline for registrations? Tuesday, 7th of March 2017, 18:00 London (UK) time

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Course Description

The powerful statistical capabilities of R programming language include a large selection of machine learning algorithms which can be applied for 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 clustering, and their practical applications through R language,

    • to present numerous third-party packages which facilitate Machine Learning 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 – a cognitive neuroscientist and an author of “Big Data Analytics with R”. Simon is Mind Project’s expert in Big Data architecture for predictive modelling and has delivered numerous Big Data and Machine Learning 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.

Programme

The course will run for 2 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.

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 available to download,

    • Wi-Fi access,

    • Central London location – a 1-min walk from the Barbican station, 5 minutes away from Farringdon and St. Paul’s stations, 15 minutes from the Liverpool Street Station,

    • networking opportunity,

    • Mind Project course attendance certificate.

Further instructions

    • In order to fully benefit from the training course, we recommend that attendees bring their personal laptops to the session with the most recent version of R and RStudio software installed and at least one of the following web browsers: Chrome, Safari, Mozilla Firefox and/or Internet Explorer. As R is a free environment you can download it directly from www.r-project.org website and RStudio is available at https://www.rstudio.com/products/rstudio/#Desktop. Please contact us should you have any questions or issues with the installation process. No specific R packages are required before the course (the course tutors will explain this during the training).

    • This course is targeted at users with some R experience (preferably at Intermediate level) 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 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.

Deadline for registrations

The deadline for registrations on this training course is Tuesday, 7th of March 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.

Prices and discounts

    • £375 + VAT (£450) per person for the whole course (regular fee).
    • £250 + VAT (£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.

Please mind that the course fee DOES NOT include the following:

    • transport to and from the venue,
    • accommodation and lunch.

Other details

Please contact us should you have any questions about this course. You may also want to visit the Training Courses – Frequently Asked Questions website, which gives further practical details about Mind Project training courses. You can book your place on the course by clicking Book ticket button in the top section of the course page. Please note that we accept all major credit/debit cards (through the PayPal and Stripe systems) and BACS payments. We can only confirm fully-paid bookings. Please contact us for other payment options e.g. if a Purchase Order is required. Please read Training & Events Terms & Conditions before your purchase.

Course feedback and testimonials

We have received the following testimonials for the previous editions of this course:

  • “Highly recommended, best place to start for machine learning with R!” – Max Fennelly, Pearson Ham
  • “Enjoyable for a course. Well done!” – Richard Knuszka
  • “A great course. I would highly recommend it!” – Kevin Connolly
  • “Enjoyed the course. The tutor is in command of his subject and patient with his students.” – Dean Allsopp, DA Consulting
  • “Good course as an introduction to machine learning, excellent details and instruction.” – Dan Leigh
  • “A very good course to get users up to speed with machine learning. Good resources to start using machine learning in my domain.”
  • “I was already passionate about R before attending the course, but even more so now!”
  • “A very interactive training course. The instructor really took his time to answer and demonstrate any ensuing questions.”

Based on the anonymous feedback forms we have also received the following comments from our attendees:

  • “Great, interactive, practical, good pace.”
  • “Good breadth, good teacher, very helpful and approachable.”
  • “The correct mix of talk and examples.”
  • “Great lecturer plus materials.”
  • “Good structure of the course. Presentations and scripts worked well, as did the examples.”
  • “A great level of detail for the amount of time.”
  • “Relaxed instructor and good examples.”
  • “I have code to take home which is well commented. Suggested reading was very good.”
  • “Good introduction with enough depth with regards to code and real life examples.”
  • “Good pace, good materials, good discussions, nice facilities, nice teacher.”

Course location

The course will be held at CAP House, 1st Floor, 9-12 Long Lane, London, EC1A 9HA. Please see the map below.

Additional Information

Ticket type

Regular fee, Discounted fee

Course Details

Start date: March 09, 2017 - March 10, 2017

Start time: 09:30

End time: 17:00

Venue: CAP House, 1st Floor, 9-12 Long Lane, London, EC1A 9HA

Phone: 0203 322 3786

Email: info@mindproject.io