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.
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~Get Your Files Here !/1. Introduction/001. Explore ensemble learning.en.srt
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~Get Your Files Here !/1. Introduction/002. What you should know.en.srt
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~Get Your Files Here !/1. Introduction/003. What tools you need.en.srt
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~Get Your Files Here !/1. Introduction/004. Using the exercise files.en.srt
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~Get Your Files Here !/2. Review Machine Learning Basics/005. What is machine learning.en.srt
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~Get Your Files Here !/2. Review Machine Learning Basics/005. What is machine learning.mp4
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~Get Your Files Here !/2. Review Machine Learning Basics/006. What does machine learning look like in real life.en.srt
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~Get Your Files Here !/2. Review Machine Learning Basics/007. What does an end-to-end machine learning pipeline look like.en.srt
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~Get Your Files Here !/2. Review Machine Learning Basics/007. What does an end-to-end machine learning pipeline look like.mp4
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~Get Your Files Here !/2. Review Machine Learning Basics/008. Bias-Variance trade-off.en.srt
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~Get Your Files Here !/2. Review Machine Learning Basics/008. Bias-Variance trade-off.mp4
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~Get Your Files Here !/3. Preparing the Data/009. Reading in the data.en.srt
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~Get Your Files Here !/3. Preparing the Data/009. Reading in the data.mp4
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~Get Your Files Here !/3. Preparing the Data/010. Cleaning up continuous features.en.srt
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~Get Your Files Here !/3. Preparing the Data/011. Cleaning up categorical features.en.srt
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~Get Your Files Here !/3. Preparing the Data/012. Write out all train, validation, and test sets.en.srt
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~Get Your Files Here !/3. Preparing the Data/012. Write out all train, validation, and test sets.mp4
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~Get Your Files Here !/4. What is Ensemble Learning/013. What is ensemble learning.en.srt
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~Get Your Files Here !/4. What is Ensemble Learning/013. What is ensemble learning.mp4
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~Get Your Files Here !/4. What is Ensemble Learning/014. How does ensemble learning work.en.srt
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~Get Your Files Here !/4. What is Ensemble Learning/015. Why is ensemble learning so powerful.en.srt
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~Get Your Files Here !/4. What is Ensemble Learning/015. Why is ensemble learning so powerful.mp4
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~Get Your Files Here !/5. Boosting/016. What is boosting.en.srt
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~Get Your Files Here !/5. Boosting/016. What is boosting.mp4
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~Get Your Files Here !/5. Boosting/017. How does boosting reduce overall error.en.srt
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~Get Your Files Here !/5. Boosting/018. When should you consider using boosting.en.srt
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~Get Your Files Here !/5. Boosting/019. What are examples of algorithms that use boosting.en.srt
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~Get Your Files Here !/5. Boosting/020. Explore boosting algorithms in Python.en.srt
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~Get Your Files Here !/5. Boosting/020. Explore boosting algorithms in Python.mp4
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~Get Your Files Here !/5. Boosting/021. Implement a boosting model.en.srt
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~Get Your Files Here !/5. Boosting/021. Implement a boosting model.mp4
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~Get Your Files Here !/6. Bagging/022. What is bagging.en.srt
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~Get Your Files Here !/6. Bagging/024. When should you consider using bagging.en.srt
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~Get Your Files Here !/6. Bagging/026. Explore bagging algorithms in Python.en.srt
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~Get Your Files Here !/6. Bagging/027. Implement a bagging model.en.srt
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~Get Your Files Here !/6. Bagging/027. Implement a bagging model.mp4
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~Get Your Files Here !/7. Stacking/028. What is stacking.en.srt
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~Get Your Files Here !/7. Stacking/031. What are examples of algorithms that use stacking.en.srt
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~Get Your Files Here !/7. Stacking/032. Explore stacking algorithms in Python.en.srt
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~Get Your Files Here !/7. Stacking/033. Implement a stacking model.en.srt
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~Get Your Files Here !/8. Conclusion/034. Compare the three methods.en.srt
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~Get Your Files Here !/8. Conclusion/034. Compare the three methods.mp4
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~Get Your Files Here !/8. Conclusion/035. Compare all models on validation set.en.srt
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~Get Your Files Here !/8. Conclusion/035. Compare all models on validation set.mp4
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~Get Your Files Here !/8. Conclusion/036. How to continue advancing your skills.en.srt
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~Get Your Files Here !/Bonus Resources.txt
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~Get Your Files Here !/Ex_Files_Applied_ML_Ensemble_Learning/Exercise Files/02_Prep_the_data/02_01/Begin/02_01.ipynb
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~Get Your Files Here !/Ex_Files_Applied_ML_Ensemble_Learning/Exercise Files/04_Boosting/04_05/Begin/04_05.ipynb
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