Machine Learning Roadmap
This is a simple, practical plan to build a strong foundation for Machine Learning. It’s intentionally brief for the assignment.
Progress: 0/7
- Python basics; Jupyter/Colab; Git
- NumPy, pandas, matplotlib
- Linear algebra (vectors, matrices)
- Calculus (derivatives, gradients)
- Probability & statistics (distributions, mean/variance)
- Supervised learning (regression, classification)
- Model evaluation (train/val/test, metrics)
- scikit-learn workflows
- Neural networks; activation, loss, optimizers
- PyTorch or TensorFlow (pick one)
- Data cleaning, feature engineering
- 2–3 small projects (tabular, images, text)
- Experiment tracking; versioning
- Share results; simple deployment options
- Bias, privacy, secure data handling
Note: This roadmap focuses on fundamentals first. Advanced topics can be added later.