https://github.com/marcozanotti/statlearning-course
Statistical Learning Course
https://github.com/marcozanotti/statlearning-course
artificial-intelligence data-science deep-learning machine-learning statistical-learning statistics
Last synced: 4 days ago
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Statistical Learning Course
- Host: GitHub
- URL: https://github.com/marcozanotti/statlearning-course
- Owner: marcozanotti
- Created: 2022-02-24T08:48:18.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2024-03-15T17:44:15.000Z (almost 2 years ago)
- Last Synced: 2024-03-15T18:57:03.923Z (almost 2 years ago)
- Topics: artificial-intelligence, data-science, deep-learning, machine-learning, statistical-learning, statistics
- Language: HTML
- Homepage: https://marcozanotti.netlify.app/
- Size: 11.7 MB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
Awesome Lists containing this project
README
---
output: github_document
editor_options:
markdown:
wrap: 72
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
# Statistical Learning, Machine Learning & Artificial Intelligence
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.
- [Website](https://marcozanotti.github.io/statlearning-course/)
- [Programme](https://marcozanotti.github.io/statlearning-course/general-infos/statlearn_syllabus.html)
## Materials
- [Lectures](https://github.com/marcozanotti/statlearning-course/tree/master/R)
- [Lecture 0 - Tidyverse](https://marcozanotti.github.io/statlearning-course/R/statlearn_lecture0_tidyverse.html)
## Suggested References
R Programming:
- [R for Data Science](https://r4ds.had.co.nz/)
- [Efficient R Programming](https://csgillespie.github.io/efficientR/index.html)
- [R Packages](https://r-pkgs.org/index.html)
- [Advanced R](https://adv-r.hadley.nz/)
Statistics & ML:
- [Feature Engineering and Selection](https://www.tidymodels.org/books/fes/)
- [Tidy Modelling with R](https://www.tmwr.org/)
- [Stacking with R](https://stacks.tidymodels.org/index.html)
- [AutoML with H2O](https://docs.h2o.ai/h2o/latest-stable/h2o-docs/index.html)
- [Deep Learning with Keras](https://keras.rstudio.com/)
Everything with R:
- [Big Book of R](https://www.bigbookofr.com/)