Jupyter notebooks and code for Machine Learning concepts, implementations, and experimental learning 🧠 📙
- Practical, clean, and structured implementations
- Designed for continuous learning and future expansion
- Covers core Machine Learning concepts with practical implementations
- Focuses on strengthening Machine Learning fundamentals
- Working with CSV files
- Working with JSON and SQL
- API to DataFrame conversion
- Web scraping using Pandas
- Understanding data using descriptive statistics
- Univariate analysis
- Bivariate analysis
- Exploratory Data Analysis (EDA)
- Pandas profiling
- Standardization
- Normalization
- Ordinal encoding
- One-hot encoding
- Handling mixed variables
- Date and time feature handling
- Feature construction and feature splitting
- Binning and binarization
- Complete case analysis
- Numerical data imputation
- Categorical data imputation
- Missing indicator
- KNN imputer
- Iterative imputer
- Outlier removal using Z-score
- Outlier removal using IQR method
- Outlier detection using percentiles
- Column Transformer
- Scikit-learn pipelines
- Function Transformer
- Power Transformer
- Principal Component Analysis (PCA)
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Regularized Linear Models
- Lasso Regression
- ElasticNet Regression
- Gradient Descent
- Types of Gradient Descent
- Logistic Regression
- Regression metrics
- Classification metrics
- Random Forest
- AdaBoost
- Gradient Boosting
- Stacking and Blending
- K-Means clustering
This repository will also cover additional topics beyond those listed above, with continuous updates and improvements.
- Python
- Env: Jupyter Notebook
- NumPy, Pandas
- Matplotlib, Seaborn
- scikit-learn
- Python installed (Python 3.8+)
- VS Code installed
- Colab extension for VS Code installed
- Stable internet connection (required for Colab server)
- Clone the repository
Clone the repo to your local machine:
git clone https://github.com/neelkumar01/Machine-Learning-Notebooks.git
cd Machine-Learning-Notebooks- Run notebooks through collab extension in VS Code
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Open any .ipynb notebook file in VS Code
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In the top-right kernel selection menu, choose “Colab”
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Click “Auto Connect” or “New Colab Server”
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Select Python 3+ as the kernel
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Run the notebook cells directly — all required libraries are pre-installed in the Colab environment
This repository is designed for:
- Students: To learn the fundamentals of Machine Learning
- Researchers: To explore practical implementations and experiments
- Developers: To understand coding and workflows in ML
- Educators: To use as a reference for teaching ML concepts
Contributions are welcome to improve this repository as a learning resource for Machine Learning. You can help by adding notebooks, improving code, fixing typos, or enhancing explanations.
- Fork the Repository – Click “Fork” on GitHub to create your own copy.
- Clone Your Fork – Clone it locally:
git clone https://github.com/neelkumar01/Machine-Learning-Notebooks.git- Create a Branch – Create a new branch for your contribution:
git checkout -b feature-name- Make Changes – Examples of contributions:
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Add new Jupyter notebooks for ML topics
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Improve existing notebook explanations or code
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Update datasets or preprocessing steps
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Correct typos or formatting in markdown cells
- Commit Changes – Use clear commit messages:
git commit -m "Add notebook for Decision Tree Regression"- Push Branch – Push your changes to your fork:
git push origin feature-name- Open a Pull Request – Open a PR to the main repository and describe your contribution
- ML community - for resources, content, research
- CampusX youtube channel
- Github repositories providing high quality codebase and resources
For any questions, suggestions, or contributions, feel free to open an issue or start a discussion in this repository. Collaboration and learning together are always welcome.