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https://github.com/rohitpawar001/machine_learning
This repository contains all the machine learning algorithms and the ml concepts.
https://github.com/rohitpawar001/machine_learning
classification hyperparameter-tuning linear-regression logistic-regression machine-learning naive-bayes numpy pandas python regression scikit-learn smote svm
Last synced: 1 day ago
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This repository contains all the machine learning algorithms and the ml concepts.
- Host: GitHub
- URL: https://github.com/rohitpawar001/machine_learning
- Owner: RohitPawar001
- Created: 2024-08-11T12:38:30.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-11-24T13:20:32.000Z (3 months ago)
- Last Synced: 2025-01-31T02:02:44.824Z (11 days ago)
- Topics: classification, hyperparameter-tuning, linear-regression, logistic-regression, machine-learning, naive-bayes, numpy, pandas, python, regression, scikit-learn, smote, svm
- Language: Jupyter Notebook
- Homepage:
- Size: 2.8 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Machine Learning Algorithms and Concepts
A comprehensive repository covering fundamental machine learning concepts, algorithms, and techniques. This repository serves as a learning resource and reference for both supervised and unsupervised machine learning approaches.
## Repository Structure
```
├── Bias_and_Variance_tradeoff/
│ └── bias_and_variance.md
├── Supervised_machine_learning_algorithms/
│ ├── classifiers/
│ ├── regressors/
│ └── Supervised_machine_learning.md
├── Unsupervised_machine_learning_algorithms/
│ ├── Anomaly_Detection/
│ ├── Dimentionality_reduction/
│ ├── clustering_algorithms/
│ └── unsupervised_machine_learning.md
├── classifications_and_regressions/
│ ├── Classifications.md
│ └── Regressions.md
├── cross_validations/
├── ensemble_techiniques/
└── performance_metrics/
```
## Contents
### 1. Supervised Learning
- **Classifiers**: Implementation and explanation of various classification algorithms
- **Regressors**: Different regression techniques and their applications
- Comprehensive guide to supervised learning concepts and methodologies
### 2. Unsupervised Learning
- **Anomaly Detection**: Techniques for identifying outliers and anomalies
- **Dimensionality Reduction**: Methods for reducing data dimensions while preserving information
- **Clustering Algorithms**: Various clustering techniques and their implementations
### 3. Core Concepts
- **Bias and Variance Tradeoff**: Understanding and managing the bias-variance tradeoff
- **Classifications and Regressions**: Detailed explanations of fundamental machine learning approaches
- **Cross Validation**: Techniques for model validation and evaluation
- **Ensemble Techniques**: Methods for combining multiple models
- **Performance Metrics**: Various metrics for evaluating model performance
## Prerequisites
To use this repository, you should have:
- Python 3.x
- Basic understanding of machine learning concepts
- Familiarity with common ML libraries (specific requirements to be added)
## Installation
```bash
# Clone the repository
git clone https://github.com/RohitPawar001/machine_learning
# Navigate to the project directory
cd machine_learning
# Install required dependencies (if any)
# pip install -r requirements.txt
```
## Usage
Each directory contains markdown files and implementations related to specific machine learning concepts. To get started:
1. Navigate to the concept you want to learn about
2. Read the corresponding markdown file for theoretical understanding
3. Check the implementation files for practical examples
4. Follow the examples and try to modify them for your own use cases
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.
## Contact
---
**Note**: This repository is for educational purposes and is continuously being updated with new content and examples.