- 01. Introduction to Machine Learning Pipeline
- 02. Data Collection
- 03. Data Pre-Processing
- 04. Feature Engineering: Transforming Raw Data into Predictive Variables
- 05. Model Selection: Choosing the Right Algorithm
- 06. Hyperparameter Tuning: Optimizing Model Performance
- 07. Model Deployment and Monitoring: Putting ML into Production
- 08. The Machine Learning Pipeline is Iterative
- 09. When to Stop Refining Your Model
- 10. Conclusion