Welcome to the Machine Learning Repository, a collection of projects, experiments, and resources focused on machine learning techniques and their applications.
This repository is a personal workspace for exploring various machine learning concepts and algorithms. It includes hands-on implementations, datasets, and detailed documentation to better understand the workings of ML models.
The repository is organized into the following sections:
- Example datasets used for training and testing models.
- Sources: Public datasets from Kaggle, UCI ML Repository, or custom-generated data.
- Implementations of popular machine learning models and algorithms, such as:
- Linear and Logistic Regression
- Decision Trees and Random Forests
- Support Vector Machines (SVM)
- Neural Networks and Deep Learning (using TensorFlow or PyTorch)
- Interactive Jupyter Notebooks for visualization and step-by-step learning.
- Includes explanations and visualizations for better understanding.