ML bootcamp homeworks
Learn the fundamentals: what ML is, when to use it, and how to approach ML problems using the CRISP-DM framework.
Topics：
- ML vs rule-based systems
- Supervised learning basics
- CRISP-DM methodology
- Model selection concepts
- Environment setup
Build a car price prediction model while learning linear regression, feature engineering, and regularization.
Topics：
- Linear regression (from scratch and with scikit-learn)
- Exploratory data analysis
- Feature engineering
- Regularization techniques
- Model validation
Create a customer churn prediction system using logistic regression and learn about feature selection.
- Logistic regression
- Feature importance and selection
- Categorical variable encoding
- Model interpretation
Learn how to properly evaluate classification models and handle imbalanced datasets.
Topics：
- Accuracy, precision, recall, F1-score
- ROC curves and AUC
- Cross-validation
- Confusion matrices
- Class imbalance handling
Turn your models into web services and deploy them with Docker and cloud platforms.
Topics：
- Model serialization with Pickle
- FastAPI web services
- Docker containerization
- Cloud deployment