https://github.com/phenomsg/ml-notebook
This project is designed for personal learning and exploration of fundamental machine learning concepts.
https://github.com/phenomsg/ml-notebook
decision-trees linear-regression logistic-regression machine-learning model-evaluation-metrics neural-network opencv pandas python3 recommendation-system sckit-learn supervised-machine-learning tensorflow2 unsupervised-machine-learning
Last synced: 3 months ago
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This project is designed for personal learning and exploration of fundamental machine learning concepts.
- Host: GitHub
- URL: https://github.com/phenomsg/ml-notebook
- Owner: PhenomSG
- License: mit
- Created: 2023-06-28T12:50:45.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-01-14T19:23:58.000Z (5 months ago)
- Last Synced: 2025-01-29T22:02:41.963Z (4 months ago)
- Topics: decision-trees, linear-regression, logistic-regression, machine-learning, model-evaluation-metrics, neural-network, opencv, pandas, python3, recommendation-system, sckit-learn, supervised-machine-learning, tensorflow2, unsupervised-machine-learning
- Language: Jupyter Notebook
- Homepage: https://phenomsg.github.io/ml-notebook/
- Size: 39.7 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ML
Welcome to the ML-Basics repository! This project is designed for personal learning and exploration of fundamental machine learning concepts. It covers a variety of topics, from basic data preprocessing to implementing different machine learning algorithms using popular libraries like Scikit-learn, TensorFlow, and PyTorch.
## Table of Contents
- [Introduction](#introduction)
- [Getting Started](#getting-started)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Directory Structure](#directory-structure)
- [Topics Covered](#topics-covered)
- [Data Preprocessing](#data-preprocessing)
- [Supervised Learning](#supervised-learning)
- [Unsupervised Learning](#unsupervised-learning)
- [Neural Networks](#neural-networks)
- [Model Evaluation](#model-evaluation)
- [License](#license)
## Introduction
This repository serves as a comprehensive guide for anyone starting out in machine learning. It includes step-by-step tutorials, code examples, and detailed explanations of various ML techniques and algorithms.
## Getting Started
### Prerequisites
To get the most out of this repository, you should have a basic understanding of Python programming and some familiarity with statistics and linear algebra. Additionally, you will need the following software installed:
- Python 3.7 or higher
- Jupyter Notebook
- Git
### Installation
1. Clone the repository:
```sh
git clone https://github.com/PhenomSG/ML-Basics.git
```
2. Navigate to the project directory:
```sh
cd ML-Basics
```
3. Create a virtual environment:
```sh
python -m venv env
```
4. Activate the virtual environment:
- On Windows:
```sh
.\env\Scripts\activate
```
- On macOS and Linux:
```sh
source env/bin/activate
```
5. Install the required packages:
```sh
pip install -r requirements.txt
```
## Topics Covered
### Data Preprocessing
- Handling missing values
- Feature scaling and normalization
- Encoding categorical variables
### Supervised Learning
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forests
- Support Vector Machines
### Unsupervised Learning
- K-means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
### Neural Networks
- Introduction to neural networks
- Building neural networks with TensorFlow and Keras
- Training and evaluating neural networks
### Model Evaluation
- Cross-validation
- Confusion matrix
- ROC curves and AUC
- Precision, recall, and F1 score
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.