7.5-fold cross-validation

8.K-Fold CV comparison

9.Regularized regression

10.Regularization I Lasso

11.Regularization II Ridge

PART 4. fine-tuning your model
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#MODULE11 #SupervisedLearning

1.How good is your model

2.Metrics for classification

3.Logistic regression and the ROC curve

4.Building a logistic regression model

5.Plotting an ROC curve

6.Area under the ROC curve

7.AUC computation

8.Hyperparameter tuning

9.Hyperparameter tuning with GridSearchCV

10.Hyperparameter tuning with RandomizedSearchCV

11.Hold-out set for final evaluation

12.Hold-out set in practice I Classification

13.Hold-out set in practice II Regression

Self Paced Videos and Slides

Module 1 - Introduction to Data Science
https://t.me/Machine_Learning_Deep_Learning/436

Module 2 - Introduction to Python
Module 3 - Pandas
Module 4 - Merging Data Frames with Pandas

Module 5 - Introduction to Data Visualisation Using Matplotlib
https://t.me/c/1454674396/8
Part- 1,2,3,4

Module 6 - Data Visualization with Seaborn
https://t.me/c/1454674396/61
Part - 1,2

Module 7 - Descriptive Statistics with Python
https://t.me/c/1454674396/106

Module 8 - Inferential Statistics with Python
https://t.me/c/1454674396/129

Module 9 - Feature Engineering
https://t.me/c/1454674396/155
Part- 1,2,3,4

Module 9.5 - Exploratory Data Analysis

Module 10 - Machine Learning for Everyone
https://t.me/Machine_Learning_Deep_Learning/225

Module 11 - Supervised Learning with scikit-learn
https://t.me/Machine_Learning_Deep_Learning/241
Part - 1,2,3,4

Module 12 - Machine Learning with Tree-Based Models in Python
https://t.me/Machine_Learning_Deep_Learning/347

Module 12.5 - Extreme Gradient Boosting with XGBoost
(No videos. Only ecercise)

Module 13 - Dimensionality Reduction and Unsupervised Learning
https://t.me/Machine_Learning_Deep_Learning/361

Module 14 - Machine Learning for Time Series Data
(No videos. Only exercise)