https://github.com/prajakta1321/creed-collection-of-ml-algos-and-strategies
Creed is a dynamic repository where machine learning algorithms meet practical implementation, crafted through hands-on coding sessions in Google Colaboratory.
https://github.com/prajakta1321/creed-collection-of-ml-algos-and-strategies
algorithms-implemented classification colaboratory emsembling feature-engineering machine-learning machine-learning-algorithms python3 pythonlibrarires regression-analysis svm-kernel
Last synced: about 2 months ago
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Creed is a dynamic repository where machine learning algorithms meet practical implementation, crafted through hands-on coding sessions in Google Colaboratory.
- Host: GitHub
- URL: https://github.com/prajakta1321/creed-collection-of-ml-algos-and-strategies
- Owner: prajakta1321
- Created: 2023-12-25T12:57:31.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-11T08:39:54.000Z (8 months ago)
- Last Synced: 2025-04-08T22:39:04.070Z (about 2 months ago)
- Topics: algorithms-implemented, classification, colaboratory, emsembling, feature-engineering, machine-learning, machine-learning-algorithms, python3, pythonlibrarires, regression-analysis, svm-kernel
- Language: Jupyter Notebook
- Homepage:
- Size: 6.06 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Creed
🎯 **Creed: A Collection of Machine Learning Algorithms and Strategies**
✅ **Overview:**
Welcome to Creed, a repository that contains a variety of small machine learning projects and algorithm implementations. This collection reflects my journey of practicing and mastering ML techniques.
✅**What You’ll Find**
This repository includes concise Python scripts focused on:
✔ Essential machine learning algorithms
✔ Optimization strategies
✔ Hands-on problem-solving techniques
✔ Tools & Environment
✅**All code is developed using:**
✔ Google Colaboratory for interactive development and testing
✔ Python for implementing the machine learning algorithms
✅**The goal of this repository is to provide:**
✔ Clear and simple examples of ML concepts
✔ Easy-to-understand code for learners and practitioners alike