This course provides a comprehensive introduction to statistical learning, machine learning, and artificial intelligence, bridging traditional statistical modeling with modern algorithmic approaches. Participants will learn how models learn from data, how to evaluate and interpret results, and how to apply these techniques to real-world problems.
The course teaches how to build end-to-end workflows using R production-ready frameworks like tidymodels, ensuring consistency, reproducibility, and scalability from model development to deployment.
By the end, participants will be able to design, tune, and assess predictive models, integrate them into robust analytical pipelines, and confidently apply state-of-the-art learning techniques across domains in business, science, and technology.
R Programming:
Statistics & ML:
- Feature Engineering and Selection
- Tidy Modelling with R
- Stacking with R
- AutoML with H2O
- Deep Learning with Keras
Everything with R: