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[ TutSala com ] Linkedin - Applied Machine Learning - Ensemble Learning
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1. Introduction
001. Explore ensemble learning.en.srt
001. Explore ensemble learning.mp4
002. What you should know.en.srt
002. What you should know.mp4
003. What tools you need.en.srt
003. What tools you need.mp4
004. Using the exercise files.en.srt
004. Using the exercise files.mp4
2. Review Machine Learning Basics
005. What is machine learning.en.srt
005. What is machine learning.mp4
006. What does machine learning look like in real life.en.srt
006. What does machine learning look like in real life.mp4
007. What does an end-to-end machine learning pipeline look like.en.srt
007. What does an end-to-end machine learning pipeline look like.mp4
008. Bias-Variance trade-off.en.srt
008. Bias-Variance trade-off.mp4
3. Preparing the Data
009. Reading in the data.en.srt
009. Reading in the data.mp4
010. Cleaning up continuous features.en.srt
010. Cleaning up continuous features.mp4
011. Cleaning up categorical features.en.srt
011. Cleaning up categorical features.mp4
012. Write out all train, validation, and test sets.en.srt
012. Write out all train, validation, and test sets.mp4
4. What is Ensemble Learning
013. What is ensemble learning.en.srt
013. What is ensemble learning.mp4
014. How does ensemble learning work.en.srt
014. How does ensemble learning work.mp4
015. Why is ensemble learning so powerful.en.srt
015. Why is ensemble learning so powerful.mp4
5. Boosting
016. What is boosting.en.srt
016. What is boosting.mp4
017. How does boosting reduce overall error.en.srt
017. How does boosting reduce overall error.mp4
018. When should you consider using boosting.en.srt
018. When should you consider using boosting.mp4
019. What are examples of algorithms that use boosting.en.srt
019. What are examples of algorithms that use boosting.mp4
020. Explore boosting algorithms in Python.en.srt
020. Explore boosting algorithms in Python.mp4
021. Implement a boosting model.en.srt
021. Implement a boosting model.mp4
6. Bagging
022. What is bagging.en.srt
022. What is bagging.mp4
023. How does bagging reduce overall error.en.srt
023. How does bagging reduce overall error.mp4
024. When should you consider using bagging.en.srt
024. When should you consider using bagging.mp4
025. What are examples of algorithms that use bagging.en.srt
025. What are examples of algorithms that use bagging.mp4
026. Explore bagging algorithms in Python.en.srt
026. Explore bagging algorithms in Python.mp4
027. Implement a bagging model.en.srt
027. Implement a bagging model.mp4
7. Stacking
028. What is stacking.en.srt
028. What is stacking.mp4
029. How does stacking reduce overall error.en.srt
029. How does stacking reduce overall error.mp4
030. When should you consider using stacking.en.srt
030. When should you consider using stacking.mp4
031. What are examples of algorithms that use stacking.en.srt
031. What are examples of algorithms that use stacking.mp4
032. Explore stacking algorithms in Python.en.srt
032. Explore stacking algorithms in Python.mp4
033. Implement a stacking model.en.srt
033. Implement a stacking model.mp4
8. Conclusion
034. Compare the three methods.en.srt
034. Compare the three methods.mp4
035. Compare all models on validation set.en.srt
035. Compare all models on validation set.mp4
036. How to continue advancing your skills.en.srt
036. How to continue advancing your skills.mp4
Bonus Resources.txt
Ex_Files_Applied_ML_Ensemble_Learning
Exercise Files
02_Prep_the_data
02_01
Begin
02_01.ipynb
ipynb_checkpoints
02_01-checkpoint.ipynb
End
02_01.ipynb
ipynb_checkpoints
02_01-checkpoint.ipynb
02_02
Begin
02_02 - 02_03.ipynb
ipynb_checkpoints
02_02 - 02_03-checkpoint.ipynb
End
02_02 - 02_03.ipynb
ipynb_checkpoints
02_02 - 02_03-checkpoint.ipynb
02_03
Begin
02_02 - 02_03.ipynb
ipynb_checkpoints
02_02 - 02_03-checkpoint.ipynb
End
02_02 - 02_03.ipynb
ipynb_checkpoints
02_02 - 02_03-checkpoint.ipynb
02_04
Begin
02_04.ipynb
ipynb_checkpoints
02_04-checkpoint.ipynb
End
02_04.ipynb
ipynb_checkpoints
02_04-checkpoint.ipynb
ipynb_checkpoints
02_01 - COMPLETED-checkpoint.ipynb
02_01-checkpoint.ipynb
02_02 - 02_03 - COMPLETED-checkpoint.ipynb
02_02 - 02_03-checkpoint.ipynb
02_02 - COMPLETED-checkpoint.ipynb
02_03 - COMPLETED-checkpoint.ipynb
02_04 - COMPLETED-checkpoint.ipynb
02_04-checkpoint.ipynb
04_Boosting
04_05
Begin
04_05.ipynb
End
04_05.ipynb
ipynb_checkpoints
04_05-checkpoint.ipynb
04_06
Begin
04_06.ipynb
ipynb_checkpoints
04_06-checkpoint.ipynb
End
04_06.ipynb
ipynb_checkpoints
04_06-checkpoint.ipynb
ipynb_checkpoints
04_05 - COMPLETED-checkpoint.ipynb
04_05-checkpoint.ipynb
04_06 - COMPLETED-checkpoint.ipynb
04_06-checkpoint.ipynb
05_Bagging
05_05
Begin
05_05.ipynb
ipynb_checkpoints
05_05-checkpoint.ipynb
End
05_05.ipynb
ipynb_checkpoints
05_05-checkpoint.ipynb
05_06
Begin
05_06.ipynb
ipynb_checkpoints
05_06-checkpoint.ipynb
End
05_06.ipynb
ipynb_checkpoints
05_06-checkpoint.ipynb
ipynb_checkpoints
05_05 - COMPLETED-checkpoint.ipynb
05_05-checkpoint.ipynb
05_06 - COMPLETED-checkpoint.ipynb
05_06-checkpoint.ipynb
06_Stacking
06_05
Begin
06_05.ipynb
End
06_05.ipynb
ipynb_checkpoints
06_05-checkpoint.ipynb
06_06
Begin
06_06.ipynb
ipynb_checkpoints
06_06-checkpoint.ipynb
End
06_06.ipynb
ipynb_checkpoints
06_06-checkpoint.ipynb
ipynb_checkpoints
06_05 - COMPLETED-checkpoint.ipynb
06_05-checkpoint.ipynb
06_06 - COMPLETED-checkpoint.ipynb
06_06-checkpoint.ipynb
07_Conclusion
07_02
Begin
07_02.ipynb
End
07_02.ipynb
models
GB_model.pkl
RF_model.pkl
stacked_model.pkl
test_features.csv
test_labels.csv
titanic.csv
titanic_cleaned.csv
train_features.csv
train_labels.csv
val_features.csv
val_labels.csv
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