Applied Machine Learning: Ensemble Learning

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Derek Jedamski

2:25:44

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1. Introduction

001. Explore ensemble learning.mp4

01:34

002. What you should know.mp4

01:13

003. What tools you need.mp4

02:06

004. Using the exercise files.mp4

01:47

2. Review Machine Learning Basics

005. What is machine learning.mp4

03:42

006. What does machine learning look like in real life.mp4

03:13

007. What does an end-to-end machine learning pipeline look like.mp4

03:23

008. Bias-Variance trade-off.mp4

05:48

3. Preparing the Data

009. Reading in the data.mp4

02:48

010. Cleaning up continuous features.mp4

05:28

011. Cleaning up categorical features.mp4

05:27

012. Write out all train, validation, and test sets.mp4

05:16

4. What is Ensemble Learning

013. What is ensemble learning.mp4

04:11

014. How does ensemble learning work.mp4

03:01

015. Why is ensemble learning so powerful.mp4

03:51

5. Boosting

016. What is boosting.mp4

04:18

017. How does boosting reduce overall error.mp4

04:25

018. When should you consider using boosting.mp4

03:41

019. What are examples of algorithms that use boosting.mp4

03:16

020. Explore boosting algorithms in Python.mp4

03:52

021. Implement a boosting model.mp4

09:16

6. Bagging

022. What is bagging.mp4

04:56

023. How does bagging reduce overall error.mp4

03:51

024. When should you consider using bagging.mp4

02:19

025. What are examples of algorithms that use bagging.mp4

04:09

026. Explore bagging algorithms in Python.mp4

02:13

027. Implement a bagging model.mp4

04:56

7. Stacking

028. What is stacking.mp4

04:28

029. How does stacking reduce overall error.mp4

01:53

030. When should you consider using stacking.mp4

02:17

031. What are examples of algorithms that use stacking.mp4

03:19

032. Explore stacking algorithms in Python.mp4

04:09

033. Implement a stacking model.mp4

06:37

8. Conclusion

034. Compare the three methods.mp4

07:17

035. Compare all models on validation set.mp4

09:54

036. How to continue advancing your skills.mp4

01:50

Description

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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Machine Learning

Derek Jedamski

Instructor's Courses

Project experience: Regression modeling (including implementation), classification (Boosting, SVM, RandomForest, MLP, etc.), natural language processing, statistical analysis, quality control, business analytics, market research, project management, version control (git), and communicating technical results to senior non-technical audiences Modeling experience: Regression (logistic, multivariate, quantile, etc), ensemble methods (Boosting, Random Forest, etc), classifiers (MLP, SVM, etc), clustering, regularization methods (ridge regression, Lasso, elastic net, etc), decision trees, dimensionality reduction (PCA, LDA, MCA, etc). Computing experience: Python, R, SQL, Scala, SAS (Certified Advanced Programmer for SAS 9), AWS, Apache Spark, Hadoop

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