Organizer: Royal Statistical Society

Venue: The Royal Statistical Society

City: London

Country: United Kingdom

Postcode: EC1Y 8LX

Description:

 

Presenters: Doug Ashton

Level: Foundation

CPD: 6 hours

This is a one day course covering the fundamentals of machine learning and the methodology for applying them to real world analytics problems. The course outlines the stages involved in a machine learning analysis, and walks through how to perform them using the R programming language and the caret library. Participants will be provided with exercises to complete through the course so as to gain hands on experience in using the methods presented.

 

Learning Outcomes

Be familiar with the overall process of how to apply Machine Learning methods in an analysis project
Understand the differences and similarities between statistical modelling and machine learning theories
Have gained hands on experience in working with the caret package in R
Gain an intuitive understanding of how several specific machine learning methods solve the problems of prediction and classification                                                     

Topics Covered

Machine Learning
Classification and Prediction
Feature Engineering
Cross Validation
Hyper-parameter Tuning
Random Forests
Gradient Boosting
Support Vector Machines 

Target Audience
Machine Learning can be applied to data in a whole range of fields from Finance to Pharmaceutical, Retail to Marketing, Sports to Travel and many, many more! This course is aimed at anyone interested in applying machine learning methods to their data in order to: gain deeper insight, make better decisions or build data products.

This course has now reached capacity. We are next running this course on 24th October 2017.

Event type:
  • Workshops and courses
Fundamentals of Machine Learning https://tess.elixir-europe.org/events/fundamentals-of-machine-learning   Presenters: Doug Ashton Level: Foundation CPD: 6 hours This is a one day course covering the fundamentals of machine learning and the methodology for applying them to real world analytics problems. The course outlines the stages involved in a machine learning analysis, and walks through how to perform them using the R programming language and the caret library. Participants will be provided with exercises to complete through the course so as to gain hands on experience in using the methods presented.   Learning Outcomes Be familiar with the overall process of how to apply Machine Learning methods in an analysis project Understand the differences and similarities between statistical modelling and machine learning theories Have gained hands on experience in working with the caret package in R Gain an intuitive understanding of how several specific machine learning methods solve the problems of prediction and classification                                                     Topics Covered Machine Learning Classification and Prediction Feature Engineering Cross Validation Hyper-parameter Tuning Random Forests Gradient Boosting Support Vector Machines  Target Audience Machine Learning can be applied to data in a whole range of fields from Finance to Pharmaceutical, Retail to Marketing, Sports to Travel and many, many more! This course is aimed at anyone interested in applying machine learning methods to their data in order to: gain deeper insight, make better decisions or build data products. This course has now reached capacity. We are next running this course on 24th October 2017. Royal Statistical Society The Royal Statistical Society, London, United Kingdom The Royal Statistical Society London United Kingdom EC1Y 8LX [] [] [] workshops_and_courses [] []