Code-Alongs: Applied Modeling
Code-Along Sessions
Code-along sessions provide hands-on practice with applied modeling concepts. Each session focuses on a specific aspect of the machine learning workflow, allowing you to apply what you've learned in the modules to real-world problems.
Code-Along 1: Feature Engineering and Imbalanced Datasets
In this code-along session, you'll work with a real-world dataset to practice feature engineering techniques and learn strategies for handling imbalanced datasets. Class imbalance is a common challenge in machine learning, especially in classification problems where one class is much more frequent than others.
What You'll Learn:
- Creating meaningful features from raw data
- Detecting and addressing class imbalance
- Applying resampling techniques (oversampling, undersampling)
- Using SMOTE and other advanced resampling methods
- Selecting appropriate evaluation metrics for imbalanced datasets
- Adjusting algorithm parameters to address imbalance
Resources:
Code-Along 2: Model Interpretation
In this code-along session, you'll explore various techniques for model interpretation, helping you understand how your models make predictions and which features have the greatest impact on those predictions.
What You'll Learn:
- Understanding model explainability and its importance
- Creating and interpreting partial dependence plots
- Using SHAP values to understand feature contributions
- Visualizing feature importance and interactions
- Communicating model insights to stakeholders
- Balancing model complexity with interpretability
Resources:
Preparing for Code-Alongs
To make the most of these code-along sessions, please take the following steps before each session:
- Download the starter notebooks from the provided links
- Ensure all required libraries are installed in your environment
- Review the relevant module(s) content related to the code-along topic
- Familiarize yourself with the dataset by reviewing any documentation
- Try to predict what challenges might arise and how you might address them
After the code-along session, compare your work with the solution notebook to identify areas for further learning. Keep in mind that there are often multiple valid approaches to solving a machine learning problem, so your solution may differ from the provided one while still being effective.