DS Unit 2 - Sprint 7: Applied Modeling

Welcome to Applied Modeling!

In this sprint, you'll learn how to build, evaluate, and interpret machine learning models for real-world problems. You'll gain hands-on experience with techniques for feature engineering, model selection, and explaining complex models to stakeholders.

Prerequisites

To get the most out of this sprint, you should have:

  • A solid understanding of Python programming fundamentals
  • Familiarity with pandas for data manipulation
  • Basic understanding of machine learning concepts and scikit-learn
  • Experience with data visualization using matplotlib and seaborn

Environment Setup: Make sure you have a working Python environment with the necessary libraries installed. Check the setup instructions in each module for specific requirements.

Module Overview

Module 1

Define ML Problems

Learn to properly define machine learning problems and set up your projects for success.

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Module 2

Wrangle ML Datasets

Master techniques for cleaning, transforming, and preparing data for machine learning models.

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Module 3

Permutation and Boosting

Explore advanced techniques for feature importance and powerful ensemble methods.

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Module 4

Model Interpretation

Learn to explain complex models and communicate insights to technical and non-technical stakeholders.

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Sprint Resources

Code-Alongs

Practice your skills with guided coding sessions:

Code-Along 1: Feature Engineering Code-Along 2: Model Interpretation

Sprint Challenge

Test your knowledge with our comprehensive sprint challenge:

Sprint Challenge: Applied Modeling

Additional Resources

Scikit-learn Documentation ELI5 Documentation SHAP Library Interpretable Machine Learning Book