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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
15.1 Conceptual / Flowchart Visuals
15.2 Statistical & Mathematical Visuals
15.3 Dimensionality Reduction & Clustering Visuals
15.4 Supervised Learning Algorithm Visuals
15.5 Deep Learning Visuals
15.6 Model Evaluation Visuals
15.7 Applied Biology Visuals
15.8 General Visual Style Guidance for Website Implementation

/Machine Learning for Biologists/PART 15 — Visual Library

Chapter 76 of 821 min read

15.2 Statistical & Mathematical Visuals

Raw vs. log transformed gene expression histograms (Ch 3.1) Gene expression matrix as a heatmap grid (Ch 3.2) PCA eigenvector direction illustration (Ch 3.2) Gradient descent "ball rolling downhill" loss curve (Ch 3.3)…

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