Machine Learning Fundamentals – Python Mini Projects

Published:

Problem

Mastering machine learning requires understanding both theory and practice — how algorithms behave with real data, how to prepare features, and how to evaluate model performance.
Goal: Build a comprehensive, hands-on portfolio of ML mini-projects that demonstrate end-to-end workflows for key algorithms in Python, each grounded in applied examples.

Approach

  • Created a structured repository with subprojects covering:
    • Regression: Linear and Polynomial Regression
    • Classification: Logistic Regression, K-Nearest Neighbors (KNN), Decision Trees, Random Forests, Support Vector Machines (SVM)
    • Ensemble Methods: Gradient Boosting, XGBoost
    • Clustering: K-Means, Hierarchical Clustering
    • Dimensionality Reduction: Principal Component Analysis (PCA)
    • Natural Language Processing (NLP): Naive Bayes text classification and TF-IDF feature extraction
    • Deep Learning: Neural Networks using TensorFlow and Keras
  • Implemented complete data preprocessing → model training → evaluation → visualization workflows using Scikit-learn and supporting libraries.
  • Emphasized algorithmic intuition through visual diagnostics (e.g., decision boundaries, feature importance, ROC curves).

Stack

  • Language: Python 3
  • Libraries: scikit-learn, pandas, numpy, matplotlib, seaborn, xgboost, tensorflow, keras
  • Tools: Jupyter Notebook, Git/GitHub
  • Concepts: supervised & unsupervised learning, model validation, scaling, feature engineering, interpretability, neural networks

Structure

Each section of the repository represents a standalone ML module from the Udemy course:

  1. Linear Regression
  2. Logistic Regression
  3. K-Nearest Neighbors (KNN)
  4. Decision Trees and Random Forests
  5. Support Vector Machines (SVM)
  6. K-Means Clustering
  7. Principal Component Analysis (PCA)
  8. Recommender Systems
  9. Natural Language Processing (NLP)
  10. Neural Nets and Deep Learning with TensorFlow and Keras
  11. Cross-validation
  12. Big Data and Spark with Python

Results

  • Developed a complete portfolio of machine learning workflows covering both predictive and unsupervised methods.
  • Strengthened understanding of data preparation, evaluation metrics, and model trade-offs.
  • Created a modular learning resource that can be extended with more advanced algorithms.

Impact

  • Establishes a solid foundation for practical ML application and interpretability.
  • Serves as a bridge between exploratory data analysis (EDA) and more advanced AI/ML workflows.
  • Complements the “Python OOP Mini-Systems” and “EDA Projects” repositories as part of a coherent progression from programming → exploration → modelling.

Links & Resources