My personal Python learning notes and practice exercises in Jupyter Notebooks, covering topics from basics to advanced concepts with practical examples.
- Python setup and installation
- NumPy: numerical computing
- Pandas: data manipulation and analysis
- Matplotlib: data visualization
- Machine Learning Algorithms:
- Linear Regression and Regularization (LASSO, Ridge)
- Support Vector Machines (SVMs)
- Decision Trees & Random Forests
- Principal Component Analysis (PCA)
- K-Means clustering
- Model evaluation:
- Cross-validation
- Model selection and comparison
These notebooks are organized for clarity and can serve as a quick reference for both learning and reviewing Python concepts.