🚀 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
  • Ensemble Learning
  • Model Tuning
  • Deep Learning
  • Recurrent Neural Networks
  • Dimensionality Reduction
  • NLP (ML approach)
  • Supervised Learning
  • Unsupervised Learning
• Roadmap
• Best Practices
• Contributing
• License

📚 Overview

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

Current Phase: Specialized ML Topics ✨

Current focus: Deep learning, ensemble learning, model tuning, and supervised/unsupervised learning.

✨ Features

• ✅ 61 notebooks — 13 NumPy, 3 NumPy exercises, 10 Pandas, 2 Pandas exercises, 7 Data Viz, 6 Statistics, 3 Foundation projects, 3 Ensemble notebooks, 2 Model Tuning notebooks, 5 Deep Learning notebooks, 2 Supervised notebooks, 2 Unsupervised notebooks, 1 Dimensionality Reduction, 2 NLP notebooks
• ✅ 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, Foundation_For_ML/2_foundation_project.ipynb, and Foundation_For_ML/3_FORD_car_price_prediction.ipynb.
• Continue with Ensemble_Learning/bagging.ipynb, Ensemble_Learning/boosting.ipynb, and Ensemble_Learning/stacking.ipynb.
• Continue with Model_Tuning/cross_validation.ipynb and Model_Tuning/grid_search_cv.ipynb.
• Move to Supervised_Learning/1_Logistic_Regression.ipynb and Supervised_Learning/2_heart_disease_pred.ipynb.
• Finish with Unsupervised_Learning/k_means_clustering.ipynb and Unsupervised_Learning/DBSCAN.ipynb.
• Explore Dimensionality Reduction with Dimensionality_Reduction/pca_dimension.ipynb.
• Try NLP notebooks in NLP( ML approach )/bag_of_words.ipynb and NLP( ML approach )/emotion_prediction.ipynb, then review the supporting model and dataset files in the same folder.
• Continue with Deep_Learning/ANN/basic_neural_network.ipynb and Deep_Learning/ANN/small_project.ipynb.
• Explore CNN with Deep_Learning/CNN/cnn.ipynb.
• Learn recurrent neural network basics with Deep_Learning/RNN/1_rnn_basics.ipynb.
• Continue with LSTM and GRU architectures in Deep_Learning/RNN/2_lstm_and_gru.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

• 3 project notebooks in Foundation_For_ML/ applying statistical and exploratory analysis to real-world datasets.

Ensemble Learning

• 3 notebooks in Ensemble_Learning/ covering bagging, boosting, and stacking.

Model Tuning

• 2 notebooks in Model_Tuning/ covering cross-validation and grid search.

Deep Learning

• 5 notebooks spanning Deep_Learning/ANN/, Deep_Learning/CNN/, and Deep_Learning/RNN/:
  • 2 ANN notebooks covering a basic neural network and a small deep learning project.
  • 1 CNN notebook covering convolutional neural networks.
  • 2 RNN notebooks covering recurrent neural networks, LSTM, and GRU architectures.

Recurrent Neural Networks

• 2 notebooks in Deep_Learning/RNN/ introducing recurrent neural networks, LSTM, and GRU architectures.

Supervised Learning

• 2 notebooks in Supervised_Learning/ covering logistic regression and heart-disease prediction.

Dimensionality Reduction

• 1 notebook in Dimensionality_Reduction/ covering PCA for dimensionality reduction.

NLP (ML approach)

• 2 notebooks in NLP( ML approach )/ covering bag-of-words and an emotion-prediction example.
• Supporting files in the same folder include a trained model artifact and dataset text used in the workflow.

Unsupervised Learning

• 2 notebooks in Unsupervised_Learning/ covering k-means clustering and DBSCAN.

📁 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
|   |-- 3_FORD_car_price_prediction.ipynb    # Ford car price prediction project
|   |-- ford.csv                             # Ford car dataset
|   |-- heart.csv                            # Heart disease dataset
|   `-- insurance.csv                        # Insurance dataset
|-- Ensemble_Learning/                      # Ensemble learning notebooks
|   |-- bagging.ipynb                       # Bagging ensemble notebook
|   |-- boosting.ipynb                      # Boosting ensemble notebook
|   `-- stacking.ipynb                      # Stacking ensemble notebook
|-- Model_Tuning/                           # Hyperparameter tuning notebooks
|   |-- cross_validation.ipynb              # Cross-validation notebook
|   `-- grid_search_cv.ipynb                # Grid-search CV notebook
|-- Deep_Learning/                          # Deep learning notebooks
|   |-- ANN/                                # Artificial neural networks
|   |   |-- basic_neural_network.ipynb      # Basic neural network example
|   |   `-- small_project.ipynb             # Small deep learning project
|   `-- CNN/                                # Convolutional neural networks
|   |   `-- cnn.ipynb                       # CNN model training and evaluation
|   `-- RNN/                                # Recurrent neural networks
|       |-- 1_rnn_basics.ipynb              # RNN basics
|       `-- 2_lstm_and_gru.ipynb            # LSTM and GRU architectures
|-- Supervised_Learning/                    # Supervised learning notebooks
|   |-- 1_Logistic_Regression.ipynb         # Logistic regression notebook
|   |-- 2_heart_disease_pred.ipynb          # Heart-disease prediction notebook
|   |-- app.py                              # Streamlit app for the prediction workflow
|   |-- columns.pkl                         # Model feature columns
|   |-- heart.csv                           # Heart disease dataset
|   |-- scaler.pkl                          # Saved scaler object
|   `-- SVM_heart_model.pkl                 # Saved SVM model
|-- Dimensionality_Reduction/               # Dimensionality reduction modules
|   `-- pca_dimension.ipynb                 # PCA dimensionality reduction
|-- NLP( ML approach )/                     # Natural Language Processing notebooks
|   |-- bag_of_words.ipynb                  # Bag-of-words example
|   |-- emotion_prediction.ipynb            # Emotion prediction example
|   |-- stacking_clf.pkl                    # Trained NLP model artifact
|   `-- train.txt                            # NLP training text data
`-- Unsupervised_Learning/                  # Unsupervised learning notebooks
    |-- k_means_clustering.ipynb            # K-means clustering notebook
    `-- DBSCAN.ipynb                        # DBSCAN clustering notebook

🗺️ 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)
• Foundation For ML (3 notebooks)
• Ensemble Learning (3 notebooks)
• Model Tuning (2 notebooks)

Upcoming Phases 🚧

• Supervised Learning — Logistic regression and classification workflows (2 notebooks)
• Unsupervised Learning — K-means clustering and DBSCAN (2 notebooks)
• Deep Learning — Neural network basics with TensorFlow/Keras (5 notebooks)
• Mini-Projects — End-to-end projects combining all skills
• Advanced 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.

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Happy Learning! 🎓

Last Updated: September 2026