Machine Learning & Generative AI Algorithm Framework
Title
Portfolio Artifact: Machine Learning & Generative AI — Algorithm Landscape
Introduction
This visual framework maps popular AI/ML algorithms to their learning styles and primary application domains—Tabular Data, Computer Vision, Natural Language Processing, and Generative AI. It serves as a quick-reference guide for choosing an algorithm based on data type and task.
Description
The infographic highlights more than ten algorithms—such as Linear/Logistic Regression, Decision Trees, Random Forests, Gradient Boosting, SVM, KNN, K-Means, PCA, CNNs, RNNs, Transformers (BERT/GPT), GANs, and Diffusion Models—and briefly explains how each works alongside real-world use cases.
Objective
Clearly identify and categorize key machine learning algorithms.
Illustrate algorithm types vs. domains in a clean, single-page visual.
Provide practical examples to guide algorithm selection for real projects.
Present a polished artifact for my professional portfolio.
Process
Scoping: Selected widely used algorithms across supervised, unsupervised, and generative paradigms.
Structuring: Grouped algorithms by domain (Tabular, CV, NLP, Generative) and aligned with learning styles.
Authoring: Drafted short “how it works” blurbs and example use cases.
Build: Used a Jupyter notebook to assemble an infographic with matplotlib (single plot, no custom colors).
Export: Produced both PNG (for web) and PDF (for print/zoom).
Tools and Technologies Used
Jupyter Notebook, Python, pandas, matplotlib
Value Proposition
This artifact demonstrates my ability to (1) organize complex ML concepts, (2) communicate them visually for stakeholders, and (3) map business problems to algorithm families quickly.
Unique Value
Combines learning styles with application domains on one page.
Pairs each algorithm with a brief mechanism and a clear use case for faster decision-making.
Relevance
Useful for interviews, technical discussions, and scoping phases of data/ML projects. It shows I can translate AI/ML concepts into concise, actionable guidance.
