Number of lectures in series: 6 two hour sessions
Pre-requisites to joining: Completion of the Data Analytics Series, or it can be demonstrated you have significant data analytics experience in the R environment covering most aspects of the Data Analytics Series.
Machine learning is increasingly influential in today’s world. This series offers an introduction to machine learning using R, and requires completion of the Data Analytics Series beforehand. The module covers algorithms like logistic regression, decision trees, neural networks, and isotrees, with a focus on coding and interpreting results rather than mathematical details. While not exhaustive, the course provides an overview of supervised and unsupervised learning, as well as classification and regression problems. It also features demonstrations on using AI to support R code development, addressing both advantages and challenges.
