Data Acquisition & Preprocessing: Understand best practices for handling missing values, encoding categories, and scaling numerical features.
Model Training & Hyperparameter Tuning: Experiment with various algorithms, grid search, and cross-validation techniques.
Performance Metrics & Interpretability: Learn to select the right metrics and explain model predictions to stakeholders.
Production-Ready ML: Discover how to containerize your model, integrate CI/CD pipelines, and monitor performance post-deployment.
 

Data Preparation & Exploration: Learn to clean, wrangle, and visualize data using libraries like Pandas and Matplotlib.
Building & Evaluating Models: Use scikit-learn to train, validate, and optimize ML models (e.g., regression, classification).
Deployment Best Practices: Explore packaging your model with frameworks like Flask or FastAPI, enabling seamless integration into applications and dashboards.