Machine Learning

Jupyter notebooks and code for Machine Learning concepts, implementations, and experimental learning 🧠 📙

📋 Overview

• 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

📚 Topics covered

Data Handling & Data Collection

• Working with CSV files
• Working with JSON and SQL
• API to DataFrame conversion
• Web scraping using Pandas

Data Understanding & Analysis

• Understanding data using descriptive statistics
• Univariate analysis
• Bivariate analysis
• Exploratory Data Analysis (EDA)
• Pandas profiling

Data Preprocessing & Feature Engineering

• Standardization
• Normalization
• Ordinal encoding
• One-hot encoding
• Handling mixed variables
• Date and time feature handling
• Feature construction and feature splitting
• Binning and binarization

Missing Value Treatment

• Complete case analysis
• Numerical data imputation
• Categorical data imputation
• Missing indicator
• KNN imputer
• Iterative imputer

Outlier Detection & Treatment

• Outlier removal using Z-score
• Outlier removal using IQR method
• Outlier detection using percentiles

Pipelines & Transformers

• Column Transformer
• Scikit-learn pipelines
• Function Transformer
• Power Transformer

Dimensionality Reduction

• Principal Component Analysis (PCA)

Regression Algorithms

• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Regularized Linear Models
• Lasso Regression
• ElasticNet Regression

Optimization Techniques

• Gradient Descent
• Types of Gradient Descent

Classification Algorithms

• Logistic Regression

Model Evaluation

• Regression metrics
• Classification metrics

Ensemble Learning

• Random Forest
• AdaBoost
• Gradient Boosting
• Stacking and Blending

Unsupervised Learning

• K-Means clustering

This repository will also cover additional topics beyond those listed above, with continuous updates and improvements.

🛠️ Tech stack

• Python
• Env: Jupyter Notebook
• NumPy, Pandas
• Matplotlib, Seaborn
• scikit-learn

⚙️ Installation and Setup

• Python installed (Python 3.8+)
• VS Code installed
• Colab extension for VS Code installed
• Stable internet connection (required for Colab server)

1. Clone the repository

Clone the repo to your local machine:

git clone https://github.com/neelkumar01/Machine-Learning-Notebooks.git
cd Machine-Learning-Notebooks

2. Run notebooks through collab extension in VS Code

• Open any .ipynb notebook file in VS Code

• In the top-right kernel selection menu, choose “Colab”

• Click “Auto Connect” or “New Colab Server”

• Select Python 3+ as the kernel

• Run the notebook cells directly — all required libraries are pre-installed in the Colab environment

🎓 Educational Value

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

🤝 Contributing

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.

How to Contribute

1. Fork the Repository – Click “Fork” on GitHub to create your own copy.
2. Clone Your Fork – Clone it locally:

git clone https://github.com/neelkumar01/Machine-Learning-Notebooks.git

3. Create a Branch – Create a new branch for your contribution:

git checkout -b feature-name

4. Make Changes – Examples of contributions:

• Add new Jupyter notebooks for ML topics

• Improve existing notebook explanations or code

• Update datasets or preprocessing steps

• Correct typos or formatting in markdown cells

5. Commit Changes – Use clear commit messages:

git commit -m "Add notebook for Decision Tree Regression"

6. Push Branch – Push your changes to your fork:

git push origin feature-name

7. Open a Pull Request – Open a PR to the main repository and describe your contribution

🙏 Acknowledgments

• ML community - for resources, content, research
• CampusX youtube channel
• Github repositories providing high quality codebase and resources

---

✉️ Contact

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.