This video is Part 3B of the series "Machine Learning Essentials for Biomedical Data Science" covering the key essentials for using machine learning as part of a data science analysis pipeline. While topics are primarily framed around applications in biomedicine, this content is broadly applicable to other domains.

The material presented was assembled based on my 15 years experience as a machine learning researcher and educator. I'm currently an Assistant Professor (Pending) of Computational Biomedicine at the Cedars Sinai Medical Center. Some of the slide content is original, with much adapted from a wide variety of textbooks, slides, and lectures by various authors and speakers. This video series expands upon a full-day workshop prepared for and presented at the Cedars Sinai Medical Center in Los Angeles. This video represents my own understanding and perspectives.

Weblinks:
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Chapters:
0:00 Introduction
0:29 Data Splitting
1:48 What Proportion To Hold Out?
2:56 Overfitting (ML Pitfall)
3:53 Validation Data
5:45 Sample Bias (ML Pitfall)
8:30 k-Fold Cross Validation
13:58 Feature Processing
15:17 Feature Engineering
18:41 Types of Missing Data (ML Pitfall)
20:10 Feature Learning
21:50 Binning
23:16 Feature Transformation
24:33 Scaling
26:46 Dimensionality Reduction
27:45 Principle Component Analysis
29:58 Unsupervised ML Algorithms
30:20 Feature Selection
31:43 Removing Redundant Features
33:37 Supervised Feature Selection
35:30 Filter-Based Feature Selection
35:59 Relief-Based Algorithms (RBAs)
39:24 ReBATE Software
39:43 Scaling RBAs in Very Large Data
41:51 Conclusion

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