Welcome to "learn-ml" – a comprehensive repository documenting my journey and projects as I explore and learn the various facets of Machine Learning (ML). This repository includes a range of projects and experiments covering key ML areas such as regression models, classification models, and clustering models, implemented using different ML algorithms and tools.
This repository serves as both a personal learning diary and a resource for others interested in understanding and applying machine learning concepts. The projects are categorized based on the type of ML model and are implemented in Python using libraries like scikit-learn, pandas, and numpy.
- Regression Models: Projects focusing on predicting continuous outcomes.
- Classification Models: Experiments with algorithms that classify data into distinct categories.
- Clustering Models: Implementations of unsupervised learning models for identifying patterns or groups in data.
- A basic understanding of Python programming.
- Familiarity with fundamental machine learning concepts.
- Clone the Repository:
git clone https://github.com/pramodyasahan/learn-ml - Install Python and Required Libraries:
- Ensure you have Python 3.x installed.
- Install libraries such as scikit-learn, pandas, numpy, and matplotlib. Use:
pip install numpy pandas scikit-learn matplotlib
Each sub-directory in this repository represents a specific area of machine learning:
/regression- Contains projects related to regression models./classification- Houses classification model experiments./clustering- Comprises clustering model implementations.
Each project directory typically contains:
- Python script(s) with the ML model implementation.
- A dataset used for training and testing (if applicable).
- A README file explaining the project specifics.
- Navigate to the project directory of your choice.
- Follow the instructions in the project's README to run the model.
Throughout this repository, I've documented my learning process and included references to learning materials such as online courses, books, and tutorials that have been particularly helpful.
Feel free to contribute to this repository:
- Fork the repository and create your feature branch.
- Submit pull requests for review and integration.
I extend my gratitude to all educators and contributors in the field of Machine Learning whose resources have been instrumental in my learning journey.