Download Linkedin - Applied Machine Learning - Ensemble Learning torrent

Linkedin - Applied Machine Learning - Ensemble Learning

Torrent:	Linkedin - Applied Machine Learning - Ensemble Learning
Description:	Applied Machine Learning: Ensemble Learning https://TutSala.com LinkedIn Learning Duration: 2h 25m \| .MP4 1280x720, 30 fps(r) \| AAC, 48000 Hz, 2ch \| 1.23 GB Genre: eLearning \| Language: English Do you want to grow your skills as a machine learning practitioner, but don’t know where to begin? You don’t need any formal training in data science to start working toward your goal. In this course, instructor Derek Jedamski shows you how to harness messy data, find signal in it, and build models that make powerful predictions with ensemble learners, one of the most common classes of machine learning algorithms. Review the basics of the machine learning pipeline to find out where ensemble learners sit within it. Learn about the underlying theory that drives ensemble learners, covering examples of ensemble learning in Python and then implementing models of your own. Explore concepts like boosting, bagging, and stacking, and how to use each and when. Get the tools you need to ramp up your predicting power and advance your machine learning skills today.
Downloads:	59
Category:	Other /Tutorials
Size:	1.2 GB
Show Files »	[ TutSala.com ] Linkedin - Applied Machine Learning - Ensemble Learning Get Bonus Downloads Here.url (0.2 KB) ~Get Your Files Here ! 1. Introduction 001. Explore ensemble learning.en.srt (2.7 KB) 001. Explore ensemble learning.mp4 (13.7 MB) 002. What you should know.en.srt (2.2 KB) 002. What you should know.mp4 (10.7 MB) 003. What tools you need.en.srt (3.8 KB) 003. What tools you need.mp4 (18.2 MB) 004. Using the exercise files.en.srt (3.1 KB) 004. Using the exercise files.mp4 (15.4 MB) 2. Review Machine Learning Basics 005. What is machine learning.en.srt (6.1 KB) 005. What is machine learning.mp4 (32.0 MB) 006. What does machine learning look like in real life.en.srt (5.5 KB) 006. What does machine learning look like in real life.mp4 (28.4 MB) 007. What does an end-to-end machine learning pipeline look like.en.srt (6.1 KB) 007. What does an end-to-end machine learning pipeline look like.mp4 (29.2 MB) 008. Bias-Variance trade-off.en.srt (10.1 KB) 008. Bias-Variance trade-off.mp4 (50.2 MB) 3. Preparing the Data 009. Reading in the data.en.srt (4.8 KB) 009. Reading in the data.mp4 (24.5 MB) 010. Cleaning up continuous features.en.srt (8.6 KB) 010. Cleaning up continuous features.mp4 (47.5 MB) 011. Cleaning up categorical features.en.srt (9.9 KB) 011. Cleaning up categorical features.mp4 (47.3 MB) 012. Write out all train, validation, and test sets.en.srt (8.1 KB) 012. Write out all train, validation, and test sets.mp4 (45.7 MB) 4. What is Ensemble Learning 013. What is ensemble learning.en.srt (7.0 KB) 013. What is ensemble learning.mp4 (36.1 MB) 014. How does ensemble learning work.en.srt (5.0 KB) 014. How does ensemble learning work.mp4 (26.1 MB) 015. Why is ensemble learning so powerful.en.srt (6.6 KB) 015. Why is ensemble learning so powerful.mp4 (33.3 MB) 5. Boosting 016. What is boosting.en.srt (7.4 KB) 016. What is boosting.mp4 (37.2 MB) 017. How does boosting reduce overall error.en.srt (7.3 KB) 017. How does boosting reduce overall error.mp4 (38.1 MB) 018. When should you consider using boosting.en.srt (6.6 KB) 018. When should you consider using boosting.mp4 (31.9 MB) 019. What are examples of algorithms that use boosting.en.srt (5.5 KB) 019. What are examples of algorithms that use boosting.mp4 (28.4 MB) 020. Explore boosting algorithms in Python.en.srt (6.5 KB) 020. Explore boosting algorithms in Python.mp4 (33.9 MB) 021. Implement a boosting model.en.srt (14.5 KB) 021. Implement a boosting model.mp4 (80.2 MB) 6. Bagging 022. What is bagging.en.srt (8.0 KB) 022. What is bagging.mp4 (42.6 MB) 023. How does bagging reduce overall error.en.srt (6.4 KB) 023. How does bagging reduce overall error.mp4 (33.3 MB) 024. When should you consider using bagging.en.srt (3.9 KB) 024. When should you consider using bagging.mp4 (20.0 MB) 025. What are examples of algorithms that use bagging.en.srt (6.9 KB) 025. What are examples of algorithms that use bagging.mp4 (35.8 MB) 026. Explore bagging algorithms in Python.en.srt (3.8 KB) 026. Explore bagging algorithms in Python.mp4 (19.2 MB) 027. Implement a bagging model.en.srt (7.9 KB) 027. Implement a bagging model.mp4 (42.9 MB) 7. Stacking 028. What is stacking.en.srt (8.3 KB) 028. What is stacking.mp4 (38.6 MB) 029. How does stacking reduce overall error.en.srt (3.2 KB) 029. How does stacking reduce overall error.mp4 (16.2 MB) 030. When should you consider using stacking.en.srt (4.3 KB) 030. When should you consider using stacking.mp4 (19.7 MB) 031. What are examples of algorithms that use stacking.en.srt (5.7 KB) 031. What are examples of algorithms that use stacking.mp4 (28.7 MB) 032. Explore stacking algorithms in Python.en.srt (6.9 KB) 032. Explore stacking algorithms in Python.mp4 (35.9 MB) 033. Implement a stacking model.en.srt (10.9 KB) 033. Implement a stacking model.mp4 (57.4 MB) 8. Conclusion 034. Compare the three methods.en.srt (12.3 KB) 034. Compare the three methods.mp4 (63.0 MB) 035. Compare all models on validation set.en.srt (17.0 KB) 035. Compare all models on validation set.mp4 (85.8 MB) 036. How to continue advancing your skills.en.srt (3.2 KB) 036. How to continue advancing your skills.mp4 (16.3 MB) Bonus Resources.txt (0.4 KB) Ex_Files_Applied_ML_Ensemble_Learning Exercise Files 02_Prep_the_data 02_01 Begin 02_01.ipynb (2.1 KB) ipynb_checkpoints 02_01-checkpoint.ipynb (2.1 KB) End 02_01.ipynb (6.9 KB) ipynb_checkpoints 02_01-checkpoint.ipynb (6.9 KB) 02_02 Begin 02_02 - 02_03.ipynb (5.2 KB) ipynb_checkpoints 02_02 - 02_03-checkpoint.ipynb (5.2 KB) End 02_02 - 02_03.ipynb (69.3 KB) ipynb_checkpoints 02_02 - 02_03-checkpoint.ipynb (69.3 KB) 02_03 Begin 02_02 - 02_03.ipynb (69.3 KB) ipynb_checkpoints 02_02 - 02_03-checkpoint.ipynb (69.3 KB) End 02_02 - 02_03.ipynb (82.1 KB) files
Added:	2022-03-04 10:06:32
Language:	English
Peers:	Seeders : 9 , Leechers : 8
Release name:	Linkedin - Applied Machine Learning - Ensemble Learning
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