Skip to content

[Sklearn] [Python] Exploring Supervised Learning models and methodologies - regularization, boosting, tuning, etc.

Notifications You must be signed in to change notification settings

d88w/udacity_ml_supervised_learning

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

47 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Udacity Data Science Nanodegree Supervised Learning

Data Science / Machine Learning / Supervised Learning

projects from the udacity data science nano degree program

  • Project_Charity_ML - Capstone Project
  1. Linear Regression from Sklearn
  2. Multi-variable Linear Regression
  3. Polynomial Regression
  4. Regularization with Lasso
  5. Feature Scaling & Lasso
  6. Decision Trees
  7. Titanic survival with Decision Trees
  8. Spam filter with Naive Bayes
  9. Tuning the Support Vector Machine (SVM)
  10. Bagging, Random Forest, AdaBoost - Spam filter
  11. Train / Test split
  12. Classification scoring metrics - accuracy_score, precision_score, recall_score, f1_score
  13. Regression scoring metrics - r2_score, mean_squared_error, mean_absolute_error
  14. Model selection using Learning Curves
  15. Model tuning / CV with Grid Search
  16. Diabetes case study - Exploring, Tuning, RandomSearchCV, FeatureImportance

About

[Sklearn] [Python] Exploring Supervised Learning models and methodologies - regularization, boosting, tuning, etc.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published