Hands-on notebooks implementing core ML algorithms with evaluations and plots.
- Status: active (Active)
- Focus: Hands-on notebooks implementing core ML algorithms with evaluations and plots.
- Last updated: 13/10/2025
- Target completion: 29/10/2025
- Hands-on Learning → Interactive Jupyter notebooks
- Core Algorithms → Linear regression, logistic regression
- Data Visualization → Matplotlib and Seaborn plots
- Real Datasets → Practical examples with real data
- Cheat Sheets → Quick reference guides
- Progressive Learning → Step-by-step complexity
notebooks/
├── course-one/ # Course 1: Supervised Machine Learning
│ ├── 01-linear-regression.ipynb
│ ├── 02-multiple-linear-regression.ipynb
│ ├── 03-logistic-regression.ipynb
│ └── 03b-logistic-regression-scikit.ipynb
cheat-sheets/
├── course-one/ # Course 1: Supervised Machine Learning
│ ├── week1-linear-regression.md
│ ├── week2-multiple-linear-regression.md
│ └── week3-logistic-regression.md
data/ # Local datasets (gitignored)
Patterns used:
- Course Organization → Structured by Machine Learning Specialization courses
- Jupyter notebooks → interactive data science workflow
- NumPy/Pandas → data manipulation and analysis
- Scikit-learn → machine learning algorithms
- Matplotlib → data visualization
- Machine learning fundamentals and algorithms
- Data science workflow and best practices
- Interactive learning with Jupyter notebooks
- Practical application of ML concepts
git clone https://github.com/Krispy145/ml-foundations.git
cd ml-foundations
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install --upgrade pip
pip install -r requirements.txt
jupyter notebook# Run notebook tests
jupyter nbconvert --execute --to notebook notebooks/*.ipynb- Notebook execution → verify all cells run successfully
- Data validation → check data loading and processing
- Visualization → ensure plots render correctly
- Follow security best practices for the technology stack
- Implement proper authentication and authorization
- Add comprehensive error handling and validation
- Set up monitoring and logging
| Milestone | Category | Target Date | Status |
|---|---|---|---|
| Complete Linear Regression | Machine Learning Specialization | 06/10/2025 | ✅ Done |
| Complete Multiple Linear Regression | Machine Learning Specialization | 07/10/2025 | ✅ Done |
| Complete Logistic Regression | Machine Learning Specialization | 26/10/2025 | ✅ Done |
| Complete Course 1: Supervised Machine Learning | Machine Learning Specialization | 26/10/2025 | ✅ Done |
| Complete Course 2: Advanced Learning Algorithms | Machine Learning Specialization | 03/11/2025 | ⏳ In Progress |
| Complete Course 3: Unsupervised Learning, Recommenders, RL | Machine Learning Specialization | 17/11/2025 | ⏳ Planned |
| Complete Machine Learning Specialization (Andrew Ng) | Machine Learning Specialization | 20/11/2025 | ⏳ Planned |
MIT © Krispy145