https://github.com/subhangisati/machine-learning
This consists of various machine learning algorithms like Linear regression, logistic regression, SVM, Decision tree, kNN etc. This will provide you basic knowledge of Machine learning algorithms using python. You'll learn PyTorch, pandas, numpy, matplotlib, seaborn, and various libraries.
https://github.com/subhangisati/machine-learning
artificial-intelligence classification data-science decision-trees knn-classification knn-model linear-regression logistic-regression machine-learning machine-learning-algorithms python regression-algorithms regression-models scikit-learn-python scikitlearn-machine-learning svm-classifier
Last synced: 4 months ago
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This consists of various machine learning algorithms like Linear regression, logistic regression, SVM, Decision tree, kNN etc. This will provide you basic knowledge of Machine learning algorithms using python. You'll learn PyTorch, pandas, numpy, matplotlib, seaborn, and various libraries.
- Host: GitHub
- URL: https://github.com/subhangisati/machine-learning
- Owner: SubhangiSati
- Created: 2024-02-02T08:38:16.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-02-10T06:20:59.000Z (over 1 year ago)
- Last Synced: 2025-01-01T06:14:11.662Z (6 months ago)
- Topics: artificial-intelligence, classification, data-science, decision-trees, knn-classification, knn-model, linear-regression, logistic-regression, machine-learning, machine-learning-algorithms, python, regression-algorithms, regression-models, scikit-learn-python, scikitlearn-machine-learning, svm-classifier
- Language: Python
- Homepage:
- Size: 17.6 KB
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: Readme.md
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README
# Basic Machine Learning Code Practice Questions
## Overview
This repository contains a collection of basic machine learning code practice questions designed to enhance your understanding of fundamental concepts and programming skills in machine learning. Each question comes with a problem statement, and you are encouraged to solve and implement the solutions using popular machine learning libraries such as TensorFlow, scikit-learn, or PyTorch.
## Prerequisites
- Python 3.x
- Basic knowledge of machine learning concepts
- Familiarity with machine learning libraries (e.g., TensorFlow, scikit-learn, PyTorch)
## Usage
1. Clone the repository to your local machine:
```bash
git clone https://github.com/your-username/basic-ml-code-practice.git
```
2. Navigate to the project directory:
```bash
cd basic-ml-code-practice
```
3. Open the questions folder and choose a question to work on.
4. Read the problem statement provided in each question's README file.
5. Implement your solution in a programming file (e.g., `solution.py`).
6. Test your solution with sample data provided or create your own test cases.
7. Optionally, you can compare your solution with the provided sample solutions.
## Questions Structure
Each question follows a common structure:
- **Problem Statement:** A clear description of the machine learning problem to be solved.
- **Instructions:** Guidelines on how to approach the problem and specific requirements for the solution.
- **Data:** If applicable, sample datasets are provided to test and validate your solution.
- **Solution:** Sample solutions are provided for comparison, but you are encouraged to implement your own solution first.
## Contributing
Feel free to contribute additional questions or improvements to existing questions. Follow the contribution guidelines outlined in the [CONTRIBUTING.md](CONTRIBUTING.md) file.
## License
This code is licensed under the [MIT License](LICENSE).
Enjoy practicing and enhancing your machine learning skills! If you find this repository helpful, consider sharing it with others.