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Machine Learning using R and Concerto (2 days)

When Sep 14, 2017 10:30 AM to
Sep 15, 2017 05:30 PM
Where Cambridge, UK
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Machine Learning using R and Concerto: an introduction for social and behavioural scientists

This practical course will teach the fundamentals of data science, statistical and machine learning using the flexible R programming environment.

On day 1, users will learn how to manipulate datasets to gain valuable insights and visualise the data in an easy-to-understand manner. They will also learn how to extract data from the World Wide Web (i.e. Wikipedia, BBC) and a social media website by either scraping or via APIs (Twitter).

On day 2, users will be given a broad overview of the commonly used machine learning algorithms and be taught how to employ them in R. They will also be able to train their own text classification model and deploy it on the web as a tool for sentiment analysis.

The course assumes some level of R programming, but no direct experience in machine learning or statistical techniques. For those who are new to R, some additional materials will be given to you prior to the workshop so that you can get familiarise with the R syntax and working environment. There are no obligations to complete the additional materials but it will certainly hasten the learning pace during the workshop.

Those with experience in these domains will still be able to find challenging content and develop their knowledge under the supervision of the experienced University of Cambridge Psychometrics Centre staff. 

LocationDates and TimeFeeTutors

Meeting Room
Psychometrics Centre
2nd Floor
JBSEEL
16 Mill Lane
Cambridge
CB2 1SB

MAP

September 14th & 15th
2017

10.30 to 17.30
on both days.
(Thursday and Friday)

Business: £600 plus VAT
Academic: £400 plus VAT
Student: £300 plus VAT

Click on above links to pay now with Credit Card

Aiden Loe and
Dr Chris Gibbons

Schedule

Day 1: Introduction to Data science using R

  • Collecting, cleaning and organising data
  • Data ‘wrangling’ using tidyverse applications
  • Web scraping techniques including data mining using application processing interfaces (APIs).

Day 2: Practical implementation of Machine Learning 

  • Types of machine learning
  • High-dimensional spaces
  • Commonly used techniques including generalised linear models, support vector machines and neural networks
  • Training machine learning algorithms
  • Side-by-side performance comparison for model performance
  • Feature selection and extraction
  • Text mining challenges including polysemy, adverbs and contronyms
  • Assessing algorithm performance using accuracy, sensitivity, and specificity
  • Ensemble learning and nFold validation
  • Web implementation of machine learning  using Concerto

Book early to avoid disappointment

Check out what our delegates say about our courses

Note: Earlier courses were held in Kuala Lumpur, Warsaw, Vienna, Sofia, Beijing, Brazil, Cambridge and Istanbul.