Welcome to the Machine Learning Code At Once repository! This collection is designed for anyone looking to learn, revise, and quickly reference essential machine learning concepts and techniques using Jupyter Notebooks.
This repository contains hands-on code, explanations, and practical examples covering a wide range of machine learning topics. All content is organized in Jupyter Notebook format for interactive learning and experimentation.
- Supervised Learning: Classification and regression models, including popular algorithms and their implementation.
- Unsupervised Learning: Clustering, dimensionality reduction, and other unsupervised techniques.
- Data Cleaning: Methods for handling missing values, outliers, and data inconsistencies.
- Data Preprocessing & Feature Engineering: Scaling, encoding, feature selection, and transformation techniques.
- Pipeline Building: End-to-end workflows for machine learning projects, from raw data to model deployment.
- Data Visualization: Exploratory data analysis and visualization using Python libraries.
- Quick Revision: Concise summaries and code snippets for fast revision before interviews or exams.
- Browse Notebooks: Each notebook focuses on a specific topic or technique. Open them in Jupyter Notebook or JupyterLab for an interactive experience.
- Experiment: Modify the code, try different datasets, and observe the results to deepen your understanding.
- Reference: Use the notebooks as a quick reference guide for common machine learning tasks and best practices.
To get started:
- Clone this repository:
git clone <repo-url>
- Install the required Python packages (see individual notebooks for dependencies).
- Open the notebooks in your preferred Jupyter environment.
- More algorithms and advanced techniques
- Real-world datasets and case studies
- Tips for model evaluation and improvement
- Additional visualization and EDA tools
Suggestions and contributions are welcome! Feel free to open issues or submit pull requests to add new notebooks or improve existing ones.
This project is for educational purposes. Please check individual notebooks for any dataset-specific licenses.
Happy Learning and Coding!