Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/xaxm007/mlchemy
A workspace related to all my Machine Learning study.
https://github.com/xaxm007/mlchemy
association-rule-learning classification-model clustering deep-neural-networks dimentionality-reduction machine-learning nlp-machine-learning pca regression-models reinforcement-learning-algorithms
Last synced: 23 days ago
JSON representation
A workspace related to all my Machine Learning study.
- Host: GitHub
- URL: https://github.com/xaxm007/mlchemy
- Owner: xaxm007
- Created: 2024-09-18T13:03:17.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-12-16T10:44:56.000Z (24 days ago)
- Last Synced: 2024-12-16T11:38:26.688Z (24 days ago)
- Topics: association-rule-learning, classification-model, clustering, deep-neural-networks, dimentionality-reduction, machine-learning, nlp-machine-learning, pca, regression-models, reinforcement-learning-algorithms
- Language: Jupyter Notebook
- Homepage:
- Size: 214 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 💻 MLchemy
A repository showcasing all my Jupyter notebooks and datasets used to implement various machine learning models during my learning journey.
---
## 📋 Table of Contents
- [Folder Structure](#folder-structure)
- [Topics Covered](#topics-covered)
- [How to Use](#how-to-use)
- [Requirements](#requirements)
---
## 📂 Folder Structure
```
MLchemy/
│
├── Regression/
│ ├── data
│ │ └── dataset.csv
│ └── notebook
│ ├── simple_linear_regression.ipynb
│ ├── support_vector_regression.ipynb
│ └── decision_tree_regression.ipynb
│
├── Classification/
│ ├── data
│ │ └── dataset.csv
│ └── notebook
│ ├── k_nearest_neighbours.ipynb
│ ├── support_vector_machine.ipynb
│ └── naive_bayes.ipynb
│
└── Dimensionality Reduction (PCA)/
├── data
│ └── dataset.csv
└── notebook
├── principal_component_analysis.ipynb
└── kernel_pca.ipynb
```
---
## 📎 Topics Covered
Machine Learning Implementations covered in this repository:
- **[Regression](./Regression/)**:
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Support Vector Regression
- Decision Tree Regression
- Random Forest Regression
- **[Classification](./Classification/)**:
- Logistic Regression
- K Nearest Neighbors
- Support Vector Machines
- Kernel SVM
- Naive Bayes
- Decision Tree Classification
- Random Forest Classification
- **[Clustering](./Clustering/)**:
- K-Means Clustering
- Hierarchical Clustering
- **[Association Rule Learning](./Association%20Rule%20Learning/)**:
- Apriori
- Eclat
- **[Reinforcement Learning](./Reinforcement%20Learning/)**
- Upper Confidence Bound
- Thompson Sampling
- **[Natural Language Processing](./Natural%20Language%20Processing/)**
- Bag of Words
- **[Deep Learning](./Deep%20Learning/)**
- Artificial Neural Networks
- Convolutional Neural Networks
- **[Dimensionality Reduction](./PCA/)**:
- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
- Kernel PCA
- **[Model Selection](./Model%20Selection/)**
- Grid Search
- k-Fold Cross Validation
---
## 👉 How to Use
To run the notebooks locally, follow these steps:
1. **Clone the repository**:
```bash
git clone https://github.com/yourusername/MLchemy.git
cd MLchemy
```
`Recommended`, Follow steps to use conda env:
1. **Create a conda env**:
```bash
conda create -n
```
2. **Install Ipykernel for using Jupyter Notebook**:
```bash
conda install ipykernel
```
3. **Connect new ipykernel to the conda env**:
```bash
python -m ipykernel install --user --name --display-name "