Livros Recomendados

Nesta seção eu reúno algumas recomendações de livros técnicos para a área de estatística, aprendizado de máquina, deep learning e ciência de dados que eu já li e achei um bom investimento de tempo.

Estatística
- A beginner’s guide to structural equation modeling - Randall E. Schumacker & Richard G. Lomax
- Bayesian Data Analysis - John K. Kruschke
- Ecological Models and Data in R - Ben Bolker
- Experiments in Ecology: Their Logical Design and Interpretation Using Analysis of Variance - A. J. Underwood
- Forecasting: Principles and Practice - Rob J. Hyndman & George Athanasopoulos
- Generalized Linear Models with Applications in Engineering and the Sciences - Raymond H. Myers, Douglas C. Montgomery, G. Geoffrey Vining & Timothy J. Robinson
- Introduction to Bayesian Statistics - William M. Bolstad & James M. Curran
- Linear Mixed-Effects Models Using R: a Step-by-Step - Andrzej Gałecki & Tomasz Burzykowski
- Probability & Statistics for Engineering and the Sciences - Jay L. Devore
Meta-Análise
- Handbook of Meta-Analysis in Ecology and Evolution - Julia Koricheva, Jessica Gurevitch & Kerrie Mengersen
Aprendizado de Máquina
- Explanatory Model Analysis - Przemyslaw Biecek & Tomasz Burzykowski
- Interpretable Machine Learning - Christoph Molnar
- Machine Learning with R - Brett Lantz
- Supervised Machine Learning for Text Analysis in R - Emil Hvitfeldt & Julia Silge
- Tidy Modelling with R - Max Kuhn & Julia Silge
- Recommender Systems - Charu C. Aggarwal
Deep Learning
- Deep Learning with Python - François Chollet
- Deep Learning with R - François Chollet & J. J. Allaire
- Dive into Deep Learning - Aston Zhang, Zachary C. Lipton, Mu Li & Alexander J. Smola
R
- R for Data Science - Hadley Wickham & Garrett Grolemund
- Text Mining with R - Julia Silge & David Robinson

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Text and figures are licensed under Creative Commons Attribution CC BY 4.0. Source code is available at https://github.com/nacmarino/codex/, unless otherwise noted. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".