🚀 Machine Learning Fundamentals

A comprehensive, hands-on learning repository documenting the journey from Python and NumPy fundamentals to practical machine learning implementation.

📋 Table of Contents

• Overview
• Features
• Project Structure
• Prerequisites & Setup
• Learning Modules
  • NumPy Fundamentals
  • Pandas Fundamentals
  • Pandas Exercises
  • Data Visualization
  • Statistics
  • Foundation For ML
• Roadmap
• Best Practices
• Contributing
• License

📚 Overview

Notebook-first workspace for building strong ML foundations with short, focused lessons and runnable examples.

Current Phase: Foundation For ML ✨

Current focus: Practical ML projects building on statistics knowledge.

✨ Features

• ✅ 43 notebooks — 13 NumPy, 3 exercises, 10 Pandas, 2 Pandas exercises, 7 data viz, 6 statistics, 2 foundation projects
• ✅ Hands-on — notebook-first, progressive difficulty
• ✅ Reproducible — pinned dependencies and setup steps

🔧 Prerequisites & Setup

Requirements

• Python 3.8+, pip, and Jupyter Notebook/Lab

Installation

1. Clone

   git clone https://github.com/gyr0byte/ML-Foundations.git "Machine Learning"
   cd "Machine Learning"

2. Create a virtual environment (recommended)

   # Windows
   python -m venv .venv
   .venv\Scripts\activate
   
   # macOS/Linux
   python -m venv .venv
   source .venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

4. Launch Jupyter

   jupyter notebook

Getting Started

• Start at NumPy/1_numpy_arrays.ipynb, finish NumPy, then move to Pandas.
• Run cells top-to-bottom and experiment with the examples.
• Move to Data Visualization with Data Visualization/matplotlib.ipynb.
• Continue with Data Visualization/Distributionplot.ipynb.
• Continue with Data Visualization/Categoricalplot.ipynb.
• Continue with Data Visualization/Matrixplot.ipynb.
• Continue with Data Visualization/Regression.ipynb.
• Continue with Data Visualization/plotlyandcufflinks.ipynb.
• Continue with Data Visualization/IPL_capstone_project.ipynb.
• Move to Statistics with Statistics/1_outliers.ipynb.
• Continue with Statistics/2_Ztest.ipynb, Statistics/3_Ttest.ipynb, Statistics/4_Two_sample_T_test.ipynb, Statistics/5_chi_square_test.ipynb, and Statistics/6_ANNOVA_test.ipynb.
• Move to Foundation For ML with Foundation For ML/1_foundation_project.ipynb and Foundation For ML/2_foundation_project.ipynb.

📖 Learning Modules

NumPy Fundamentals

• 13 core notebooks + 3 exercises in NumPy/.

Pandas Fundamentals

• 10 core notebooks in Pandas/, including IPL analysis, company data, and Titanic survival analysis.

Pandas Exercises

• 2 practice notebooks in Pandas/pandas_exercise/.

Data Visualization

• 7 notebooks in Data Visualization/ covering Matplotlib basics, distribution plots, categorical plots, matrix plots, regression plots, Plotly/Cufflinks, and an IPL capstone.
• Assets: Data Visualization/basic_plot.png, Data Visualization/zoro.jpg.

Statistics

• 6 notebooks in Statistics/ covering outlier detection and handling, Z-test, T-test, Two-sample T-test, Chi-square test, and ANOVA test hypothesis testing.

Foundation For ML

• 2 project notebooks in Foundation For ML/ applying statistical and exploratory analysis to real-world datasets.

📁 Project Structure

Machine Learning/
|-- README.md                               # Project overview and guide
|-- requirements.txt                        # Python dependencies
|-- LICENSE                                 # License for reuse and distribution
|-- .gitignore                              # Git ignore patterns
|-- Data Visualization/                     # Data visualization modules
|   |-- matplotlib.ipynb                    # Matplotlib basics and plots
|   |-- Distributionplot.ipynb              # Distribution plots
|   |-- Categoricalplot.ipynb               # Categorical plots
|   |-- Matrixplot.ipynb                    # Matrix plots
|   |-- Regression.ipynb                    # Regression plots
|   |-- plotlyandcufflinks.ipynb            # Plotly and Cufflinks
|   |-- IPL_capstone_project.ipynb          # IPL capstone project
|   |-- IPL.csv                             # IPL dataset
|   |-- basic_plot.png                      # Sample plot image asset
|   `-- zoro.jpg                            # Image asset used in notebooks
|-- NumPy/                                  # NumPy fundamentals modules
|   |-- 1_numpy_arrays.ipynb                # Arrays basics
|   |-- 2_arrays_types.ipynb                # Data types (dtypes)
|   |-- 3_dimension_shapes.ipynb            # Dimensions & shapes
|   |-- 4_indexing_slicing_iteration.ipynb  # Advanced indexing
|   |-- 5_statistics.ipynb                  # Statistical operations
|   |-- 6_broadcasting_vectorize.ipynb      # Broadcasting & vectorization
|   |-- 7_boolean_arrays.ipynb              # Boolean indexing
|   |-- 8_linear_algebra.ipynb              # Linear algebra operations
|   |-- 9_size_of_objectsInMemory.ipynb     # Memory size exploration
|   |-- 10_useful_numpy_function.ipynb      # Useful NumPy utilities
|   |-- 11_numpy_operations.ipynb           # NumPy operations overview
|   |-- 12_Reshaping_inDepth.ipynb          # Reshaping deep dive
|   |-- 13_plotting_graphs_numpy.ipynb      # Plotting graphs with NumPy
|   `-- numpy_exercises/                    # Practice notebooks
|       |-- general_qns.ipynb               # Mixed practice questions
|       |-- nepali_cricket_score.ipynb      # Practice with real-world data
|       `-- valid_sudoku.ipynb              # NumPy practice exercise
|-- Pandas/                                 # Pandas fundamentals modules
|   |-- 1_series.ipynb                      # Series basics
|   |-- 2_DataFrames.ipynb                  # DataFrames basics
|   |-- 3_Missing_Data.ipynb                # Missing data handling
|   |-- 4_Merging_Joining_Concatination.ipynb # Merging and joining
|   |-- 5_GroupByAggregation.ipynb          # GroupBy and aggregation
|   |-- 6_pivot_tables.ipynb                # Pivot tables and reshaping
|   |-- 7_Operations.ipynb                  # Pandas operations
|   |-- 8_ipl_analysis.ipynb                # IPL data analysis
|   |-- 9_company.ipynb                     # Company data analysis
|   |-- 10_titanic.ipynb                    # Titanic survival analysis
|   |-- deliveries.csv                      # IPL deliveries dataset
|   |-- ipl_matches.csv                     # IPL matches dataset
|   |-- Fortune_500_Companies.csv           # Company dataset
|   |-- titanic_data.csv                    # Titanic passenger dataset
|   `-- pandas_exercise/                    # Pandas practice notebooks
|       |-- Countries.csv                   # Sample dataset
|       |-- Countries.ipynb                 # Country data practice
|       |-- feature_extraction.ipynb        # Feature extraction practice
|       `-- topanime.csv                    # Sample dataset
|-- Statistics/                             # Statistical methods modules
    |-- 1_outliers.ipynb                    # Outlier detection and handling
    |-- 2_Ztest.ipynb                       # Hypothesis testing: Z-test
    |-- 3_Ttest.ipynb                       # Hypothesis testing: T-test
    |-- 4_Two_sample_T_test.ipynb           # Hypothesis testing: Two-sample T-test
    |-- 5_chi_square_test.ipynb             # Hypothesis testing: Chi-square test
    `-- 6_ANNOVA_test.ipynb                 # Hypothesis testing: ANOVA test
`-- Foundation For ML/                      # Foundation ML projects
    |-- 1_foundation_project.ipynb          # Foundation ML project 1
    |-- 2_foundation_project.ipynb          # Foundation ML project 2
    |-- heart.csv                           # Heart disease dataset
    `-- insurance.csv                       # Insurance dataset

🗺️ Roadmap

Current Phase ✅

• NumPy fundamentals (13 modules)
• NumPy exercises (3 notebooks)
• Pandas fundamentals (10 notebooks)
• Pandas exercises (2 notebooks)
• Data visualization (7 notebooks)
• Statistical methods (6 notebooks)

Upcoming Phases 🚧

• [/] Foundation For ML — Practical ML projects with real datasets (2 notebooks)
• Supervised Learning — Regression and classification with Scikit-Learn
• Unsupervised Learning — Clustering and dimensionality reduction
• Mini-Projects — End-to-end projects combining all skills
• Deep Learning — Introduction to TensorFlow/PyTorch

🏆 Best Practices

• Follow PEP 8 and use descriptive names
• Keep cells focused with short markdown context
• Pin dependencies in requirements.txt

🤝 Contributing

Suggestions and improvements are welcome.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/improvement)
3. Make your changes
4. Commit your changes (git commit -am 'Add improvement')
5. Push to the branch (git push origin feature/improvement)
6. Open a Pull Request

📄 License

This project is licensed under the MIT License — see the LICENSE file for details.

---

Happy Learning! 🎓

Last Updated: June 2026