Loading chapter

Loading courses

ComputingLifeSci

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

support@computinglifesci.com 8052536359

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

Home All courses Roadmaps Videos BINC & GATE notes Career Know where you can work Pricing About Contact us Terms Privacy Refunds & delivery

Loading chapter

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 18 of 821 min read

Overview

Every algorithm chapter below follows the same practical checklist requested: Intuition, Mathematics, Visual explanation, Biological examples, Advantages, Disadvantages, Assumptions, Hyperparameters, R implementation,…

Sign in to read this

“Overview” is for subscribers. Start with a free account — it takes a name and an email — then subscribe for $1 a month to open it.

Python and R courses free in full

First two chapters of every other course

Everything else — including this one — for $1 a month

Cancel any time

Create a free account I already have one

PreviousChapter 4.4 — Dimensionality Reduction: PCA, t-SNE, and UMAP NextChapter 5.1 — Linear, Multiple, and Polynomial Regression