A structured collection of machine learning concepts, implementations, experiments, and results developed using Python, Scikit-learn, and PyTorch.
| Module | Topics |
|---|---|
| 01. Machine Learning Basics | Introduction, Supervised & Unsupervised Learning |
| 02. Dataset Preparation | Data Loading, Feature Selection, Train-Test Split |
| 03. Data Preprocessing | Missing Values, Mean Imputation, Outlier Detection |
| 04. Model Evaluation | Confusion Matrix, Accuracy, Precision, Recall, F1-Score, ROC Curve |
| 05. K-Nearest Neighbors (KNN) | KNN , Euclidean Distance, Hyperparameter Tuning |
| 06. Deep Learning | Artificial Neural Networks (ANN) and Generative Adversarial Networks (GAN) |
- Python
- NumPy
- Pandas
- Scikit-learn
- PyTorch
- Matplotlib
- Machine Learning concepts with learning notes
- Python implementations for each module
- Model evaluation and performance analysis
- KNN implemented from scratch
- GAN implementation using PyTorch (CIFAR-10)
This repository documents my learning journey in Machine Learning, combining theoretical concepts with practical implementations and experimental results.