https://github.com/mrfoxak/artificial-intelligence
This is All About AI & ML
https://github.com/mrfoxak/artificial-intelligence
airtificialintelligence data-science dataanalysis datapreprocessing datavisualization deep-learning feature-engineering feature-extraction feature-selection jyputer-notebook machine-learning machine-learning-algorithms natural-language-processing neural-network python
Last synced: 4 months ago
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This is All About AI & ML
- Host: GitHub
- URL: https://github.com/mrfoxak/artificial-intelligence
- Owner: MrfoxAK
- Created: 2024-09-03T21:02:29.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-18T12:38:03.000Z (about 1 year ago)
- Last Synced: 2025-06-23T12:44:01.865Z (7 months ago)
- Topics: airtificialintelligence, data-science, dataanalysis, datapreprocessing, datavisualization, deep-learning, feature-engineering, feature-extraction, feature-selection, jyputer-notebook, machine-learning, machine-learning-algorithms, natural-language-processing, neural-network, python
- Language: Jupyter Notebook
- Homepage:
- Size: 39 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: History_of_Ai/History_of_Ai.png
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README
# AI, Machine Learning, and Deep Learning Repository
)
Welcome to the **All-in-One** repository, where you'll find everything you need to get started with **Artificial Intelligence (AI)**, **Machine Learning (ML)**, and **Deep Learning (DL)**! This repo is a comprehensive collection of my work, including **machine learning algorithms**, **deep learning models**, and various **AI projects** that showcase the versatility and potential of these technologies.
## Table of Contents
- [Overview](#overview)
- [Machine Learning Algorithms](#machine-learning-algorithms)
- [Deep Learning Models](#deep-learning-models)
- [Projects](#projects)
- [Installation](#installation)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)
## Overview
This repository is designed as a one-stop destination for all things related to AI, ML, and DL. Whether you're a beginner or an advanced user, you'll find valuable resources, code examples, and full-fledged projects that can help you deepen your understanding and skills.
## Machine Learning Algorithms
Here are the **ML algorithms** implemented in this repository:
- Linear Regression
- Decision Trees
- Random Forests
- K-Nearest Neighbors (KNN)
- Support Vector Machines (SVM)
- Gradient Boosting Machines
- K-Means Clustering
- And more...
Each algorithm is well-documented and includes:
- Code implementation in Python
- Dataset (where applicable)
- Step-by-step explanations
## Deep Learning Models
Explore **DL models** built using frameworks like TensorFlow, Keras, and PyTorch:
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Long Short-Term Memory (LSTM)
- Autoencoders
- Generative Adversarial Networks (GANs)
- Transfer Learning Models
- And more...
These models include training scripts, evaluation results, and pre-trained weights for ease of use.
## Projects
This repo also features various **AI/ML/DL projects**, including:
- Image classification using CNN
- Time series forecasting
- Natural language processing (NLP) tasks like text classification
- Reinforcement learning simulations
- Predictive models for structured data (e.g., sales forecasting, recommendation systems)
Each project includes:
- Problem statement
- Data preprocessing
- Model training and evaluation
- Results and discussion
## Installation
To run any of the scripts or projects in this repository, follow these steps:
1. Clone the repository:
```bash
git clone https://github.com/yourusername/repo-name.git
```
2. Navigate to the cloned directory:
```bash
cd repo-name
```
3. Install the necessary dependencies:
```bash
pip install -r requirements.txt
```
## Usage
Detailed instructions for each algorithm and project are available within their respective folders. For example, to run a machine learning algorithm:
1. Navigate to the specific folder (e.g., `machine-learning/linear-regression`).
2. Run the script:
```bash
python linear_regression.py
```
For deep learning models, make sure you have the appropriate hardware (e.g., GPU) to efficiently train the models.
## Contributing
Contributions are welcome! Feel free to open an issue or submit a pull request if you'd like to add new algorithms, improve existing code, or contribute new projects.
## License
This repository is licensed under the MIT License. See the [LICENSE](LICENSE) file for more information.