This week is all about improving ML workflows with practical tools and techniques:

• Missing data is handled using methods like imputation instead of dropping values.

• Imputation fills in missing values to keep datasets usable.

• Pipelines streamline the entire ML process by chaining preprocessing and modeling steps together, helping prevent data leakage.

• Validation techniques, like train/test splits and cross-validation, ensure your model performs well on unseen data.

These tools help build more reliable, efficient, and accurate machine learning models.

🎯 What You'll Learn:

•⁠ ⁠Handle messy data like a pro (missing values & categories)

•⁠ ⁠Validate models that actually work in production

•⁠ ⁠Build streamlined ML pipelines for your dev workflow

📚 Required: Intermediate ML (Kaggle) - https://www.kaggle.com/learn/intermediate-machine-learning 

Your laptop will also be handy😉

📘 Recommended: Hands-On ML Ch. 3-5, Scikit-learn Pipelines

🧪 Hands-On Practice:

•⁠ ⁠Create pipelines that fit your development stack

•⁠ ⁠Compare models to find what works for YOUR apps

✅ Take Away:

•⁠ ⁠Working notebook with complete pipeline implementation

•⁠ ⁠Documentation to justify your approach to teammates

*Join developers from web, mobile, cloud & AI to build smarter applications together!*