A Pragmatic Approach to Machine Learning with R

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Created by: Richard Churchman
A pragmatic approach to Machine Learning with R for 2021

What Will I Learn?

• How to apply methodology to assure the success of machine learning projects.
• How to comfortably replace Excel with R, never returning to Excel for analysis again!
• How to import, shape, mould, filter, arrange and aggregate date in R instead of using a database for the same.
• How to make assertions about data with summary statistics and make one variable predictions.
• How to express domain experience given data and the overarching importance of Abstraction.
• How to make classifications with Logistic Regression, C5 Decision Trees and Neural Networks.
• How to make numeric predictions with Linear Regression, Regression Trees and Neural Networks.
• How to make pragmatic use of visualisations.
• How to use subjective probability and know its function in Bayesian networks.
• How to use Monte Carlo simulation with numeric predictions or classifications for the purposes of optimisation.
• How to integrate models as high performance HTTP microservices.

Requirements

• Interest and curiosity about machine learning.
• A basic background in Excel only.
• A project to move onto immediately on conclusion of the course to avoid skill fade.

Target audience

• Managers who want to lean more advanced analytical techniques and machine learning.
• Data analysts who want to learn more advanced analytical techniques and machine learning.
• Junior analysts who want to accelerate their career by taking on an advanced analytics or machine learning project on their own initiative.