Applied Machine Learning: Foundations

Develop foundational skills and technical know-how for dealing with real-world problems using the Python ecosystem.

Introduction

• Mastering machine learning essentials
• What you should know

1. Introduction to Machine Learning

• Overview of types of machine learning
• Applications of ML
• Tools for ML
• Using GitHub Codespaces with this course

2. EDA

• Exploring the dataset
• Data preprocessing
• Scikit-learn pipelines
• Challenge: EDA plot
• Solution: EDA plot

3. Model Creation

• Dummy model
• Linear regression
• Decision trees
• CatBoost
• Challenge: Random forest pipeline
• Solution: Random forest pipeline

4. Model Evaluation

• R2
• Root mean squared
• Residual plot
• Challenge: Evaluate random forest
• Solution: Evaluate random forest

5. Model Tuning

• Hyperparameters and linear regression
• Tuning decision trees
• Tuning CatBoost
• Grid search
• Challenge: Tuning random forest
• Solution: Tuning random forest

6. Model Deployment

• End-to-end notebook
• Using MLFlow
• Challenge: MLFlow with random forest
• Solution: MLFlow with random forest

Conclusion

• Next steps