resources.Rmd

---
title: "Resources"
output:
  distill::distill_article:
    pandoc_args: null
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

# Books

- ML
  * [Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems | Amazon](https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646)
  * [Hands-On Machine Learning with R (HOMLR)](https://bradleyboehmke.github.io/HOML/) (freely available online)
    - [data-science-learning-resources](https://github.com/bradleyboehmke/data-science-learning-resources#natural-language-processing--text-mining) from HOMLR author
  * [Machine Learning with R: Expert techniques for predictive modeling | Amazon](https://www.amazon.com/Machine-Learning-techniques-predictive-modeling/dp/1788295862)
  * [Section V Machine Learning | Introduction to Data Science](https://rafalab.github.io/dsbook/introduction-to-machine-learning.html)
- ML & Env
  * [Machine Learning for Ecology and Sustainable Natural Resource Management | Springer](https://link.springer.com/book/10.1007/978-3-319-96978-7)
  * [Habitat Suitability and Distribution Models: With Applications in R |  Amazon](https://www.amazon.com/Habitat-Suitability-Distribution-Models-Applications-ebook/dp/B0744JXDPF)

# Courses

- ML
  * [Machine Learning | Coursera](https://www.coursera.org/learn/machine-learning)
  * [Courses | Kaggle](https://www.kaggle.com/learn)