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ComputingLifeSci

A complete computational biology curriculum — 23 courses covering programming, databases, machine learning, genomics and research infrastructure. Written for biologists, not computer scientists.

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Programming for Biology

Python for Biologists
R for Biologists
Perl for Biologists
MATLAB for Biologists
SAS for Biologists
BioJava for Biologists

Data & Databases

SQL for Biological Databases
Bioinformatics Databases
Big Data Mining for Biologists

AI & Machine Learning

Machine Learning for Biologists
Artificial Intelligence for Biologists
MLOps for Biologists
AI Tools for the Bioinformatics Lab

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Machine Learning for Biologists82 chapters

Chapter 11.1 — R Meta-Frameworks: caret and the tidymodels Suite
Chapter 11.2 — R Algorithm Packages
Chapter 11.3 — scikit-learn (Python)
Chapter 11.4 — Deep Learning Frameworks: TensorFlow/Keras and PyTorch
Chapter 11.5 — Boosting Libraries: XGBoost, LightGBM, and CatBoost (Python)
Chapter 11.6 — Hyperparameter Tuning and Explainability: Optuna, SHAP, and LIME (Python)

/Machine Learning for Biologists/PART 11 — Explain Every Package

Chapter 47 of 822 min read

Chapter 11.2 — R Algorithm Packages

Package Installation Purpose Key Functions Common Errors Best Practice Classic Random Forest implementation (Ch 5.4) , , Factor vs. numeric outcome confusion (determines classification vs. regression mode) Set if you…

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