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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

Overview
Chapter 5.1 — Linear, Multiple, and Polynomial Regression
Chapter 5.2 — Logistic Regression and Naive Bayes
Chapter 5.3 — K-Nearest Neighbors (KNN) and Decision Trees
Chapter 5.4 — Random Forest and Extra Trees
Chapter 5.5 — Gradient Boosting: AdaBoost, XGBoost, LightGBM, and CatBoost
Chapter 5.6 — Support Vector Machines (SVM)
Chapter 5.7 — Neural Networks (Foundations Before Deep Learning)

/Machine Learning for Biologists/PART 5 — Supervised Learning

Chapter 24 of 826 min read

Chapter 5.6 — Support Vector Machines (SVM)

1. Learning Objectives Understand SVM as finding the "widest street" separating classes Understand the kernel trick for non linear boundaries Fit and interpret an SVM in R and Python 2. Theory SVM (Support Vector…

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PreviousChapter 5.5 — Gradient Boosting: AdaBoost, XGBoost, LightGBM, and CatBoost NextChapter 5.7 — Neural Networks (Foundations Before Deep Learning)