https://github.com/aryehky/ml-theory-to-implementation
📚🧠 Explores core machine learning concepts through interactive, hands-on Jupyter notebooks. From building a neural network from scratch to applying dimensionality reduction and classification with real-world libraries, this project bridges the gap between theory and practical application.
https://github.com/aryehky/ml-theory-to-implementation
4d-database jupyter-notebook machine-learning matploblib numpy pca-analysis
Last synced: 11 months ago
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📚🧠 Explores core machine learning concepts through interactive, hands-on Jupyter notebooks. From building a neural network from scratch to applying dimensionality reduction and classification with real-world libraries, this project bridges the gap between theory and practical application.
- Host: GitHub
- URL: https://github.com/aryehky/ml-theory-to-implementation
- Owner: aryehky
- Created: 2025-05-23T16:56:28.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-23T19:25:57.000Z (about 1 year ago)
- Last Synced: 2025-06-04T22:31:39.763Z (12 months ago)
- Topics: 4d-database, jupyter-notebook, machine-learning, matploblib, numpy, pca-analysis
- Language: Jupyter Notebook
- Homepage:
- Size: 347 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🧠 ML Foundations: Theory to Practice
This repository explores core machine learning concepts through interactive, hands-on Jupyter notebooks. From building a neural network from scratch to applying dimensionality reduction and classification with real-world libraries, this project bridges the gap between theory and practical application.
---
## 📚 Notebooks Overview
| Notebook | Focus | Description |
| -------------------------------- | ------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------- |
| `01_pca_dimensionality.ipynb` | 📉 Dimensionality Reduction | Uses Principal Component Analysis (PCA) to reduce high-dimensional data while preserving variance. Ideal for feature compression and visualization. |
| `02_neural_net_numpy.ipynb` | 🤖 Neural Network from Scratch | Implements a simple feedforward neural network using only NumPy. Covers forward propagation, backpropagation, and a training loop. |
| `03_classification_models.ipynb` | 🧪 ML Classification | Applies supervised learning techniques using scikit-learn. Includes data preprocessing, model training, evaluation, and comparison. |
---
## 🔍 Key Takeaways
### 🎯 PCA
* Reduced a 4D dataset to 2D while retaining \~95% of variance.
* Visualized separability of classes in reduced space.
### 🛠️ Custom Neural Network
* Built from scratch using NumPy (no ML frameworks).
* Trained a neural net using manual backpropagation and gradient descent.
* Demonstrated convergence on a synthetic dataset.
### 📈 Classification Pipeline
* Achieved \~97% accuracy using logistic regression.
* Visualized results via a confusion matrix.
* Compared multiple models including SVM and Decision Trees.
---
## 🧰 Tech Stack
* **Python** – Core scripting language
* **NumPy** – Neural network implementation
* **Scikit-learn** – PCA, classification, evaluation tools
* **Matplotlib & Seaborn** – Visualization
* **Jupyter Notebooks** – Interactive development
---
## 📁 Directory Structure
```
ml-theory-to-practice/
├── 01_pca_dimensionality.ipynb # Dimensionality Reduction with PCA
├── 02_neural_net_numpy.ipynb # Neural Network built from scratch
├── 03_classification_models.ipynb # Supervised ML classification models
└── README.md
```
---
## 🚀 Project Goals
* Solidify foundational machine learning theory through hands-on implementation
* Develop intuition for model behavior, training dynamics, and evaluation
* Showcase practical skills for portfolio use or collaborative work