https://github.com/niranjanrao07/data-245-assignments
This repository documents my journey through a curriculum focused on classical machine learning techniques, blending both theoretical foundations and hands-on implementation.
https://github.com/niranjanrao07/data-245-assignments
binaryclassification gwar knn linear-regression machine-learning math python svm
Last synced: 25 days ago
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This repository documents my journey through a curriculum focused on classical machine learning techniques, blending both theoretical foundations and hands-on implementation.
- Host: GitHub
- URL: https://github.com/niranjanrao07/data-245-assignments
- Owner: NiranjanRao07
- Created: 2025-03-12T18:50:55.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-12T18:57:39.000Z (over 1 year ago)
- Last Synced: 2025-07-24T04:50:07.073Z (11 months ago)
- Topics: binaryclassification, gwar, knn, linear-regression, machine-learning, math, python, svm
- Language: Jupyter Notebook
- Homepage:
- Size: 256 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Machine Learning Course Assignments Repository
Welcome to my repository for completed assignments in the Machine Learning course! This repository documents my journey through a curriculum focused on classical machine learning techniques, blending both theoretical foundations and hands-on implementation.
## Repository Overview
- **Technical Projects & Assignments:**
Detailed implementations of various machine learning models applied to diverse datasets. Each project includes exploratory data analysis, model training, evaluation, and optimization.
- **Research & Writing (GWAR Component):**
In-depth research papers, literature reviews, and reports discussing the theoretical underpinnings of the techniques used. This component also emphasizes the critical writing skills required to effectively communicate research findings and technical insights.
- **Hands-on Implementation:**
Practical assignments that challenge you to apply classical ML techniques on real-world datasets, showcasing both the strengths and limitations of these methods.
## Organization
The repository is organized by assignment and project, with each folder containing:
- Source code and Jupyter Notebooks for practical exercises
- Data files and model outputs (where applicable)
- Detailed documentation and write-ups on the approach, methodology, and results