R_Py Data Science - VideoAndMovie

Hello my dear, welcome to this channel. I hope you are having a great day. My name is Dr. Kebede and I am an epidemiologist. If you want to learn more about me, I encourage you to visit my website(linked below). I also occasionally blog to share with you some of my data science experiences.

Part 17: Pooling odds ratios from scratch (the Peto method)

28:09

Part 17: Pooling odds ratios from scratch (the Peto method)

part 16: Meta-analysis pf OR (MH method) from scratch (in R)

30:41

part 16: Meta-analysis pf OR (MH method) from scratch (in R)

Correlation Heatmap: Python Vs R

0:34

Correlation Heatmap: Python Vs R

Part 15: Meta-analysis of Risk Ratios using metabin and metagen in R

26:45

Part 15: Meta-analysis of Risk Ratios using metabin and metagen in R

Part 15: Meta-analysis of precalculated odds ratios using meta package(metagen)

9:41

Part 15: Meta-analysis of precalculated odds ratios using meta package(metagen)

Part 14: Meta-analysis of odds ratios using meta package

11:39

Part 14: Meta-analysis of odds ratios using meta package

Part 13: Meta-analysis of Binary Outcomes

6:31

Part 13: Meta-analysis of Binary Outcomes

Part 12: Meta-analysis of correlations

18:00

Part 12: Meta-analysis of correlations

Part 11: Pooling MD and precalculculated effect sizes in random effects meta-analysis

17:51

Part 11: Pooling MD and precalculculated effect sizes in random effects meta-analysis

Part 10: Random effect meta-analysis(poolin SMD) in R

18:29

Part 10: Random effect meta-analysis(poolin SMD) in R

Part 9: Random Effects Meta-analysis

15:38

Part 9: Random Effects Meta-analysis

Part 8: Pooling SMD from scratch (Meta-analysis in R)

20:31

Part 8: Pooling SMD from scratch (Meta-analysis in R)

Part 7: Pooling Mean difference(Fixed effects meta-analysis in R)

24:17

Part 7: Pooling Mean difference(Fixed effects meta-analysis in R)

Part 6: Pooling effects sizes in R (Fixed effect meta-analysis)

24:23

Part 6: Pooling effects sizes in R (Fixed effect meta-analysis)

Part 5: Effect size mesures for binary outcomes (Meta-anylsis in R)

22:12

Part 5: Effect size mesures for binary outcomes (Meta-anylsis in R)

Part 4: Standardized mean difference

21:02

Part 4: Standardized mean difference

Part 3: Effect size in control group designs

19:15

Part 3: Effect size in control group designs

Part 2: Effect size measures in single group designs

15:53

Part 2: Effect size measures in single group designs

Part I: Introduction to Meta-analysis

12:15

Part I: Introduction to Meta-analysis

Part 24: Ensemble methods for combining text classification predictions in R

19:00

Part 24: Ensemble methods for combining text classification predictions in R

Part 23: Elastic Net regularization for biomedical literature text classification

26:13

Part 23: Elastic Net regularization for biomedical literature text classification

Part 22: Lasso regression for biomedical literature text classification in R

22:28

Part 22: Lasso regression for biomedical literature text classification in R

Part 21: Ridge regression for biomedical literature text classification

29:32

Part 21: Ridge regression for biomedical literature text classification

Part 20: Regularized Regression Models: Ridge, LASSO and Elastic Net

32:27

Part 20: Regularized Regression Models: Ridge, LASSO and Elastic Net

Part 19: Saving several text classification models & extracting performance metrics in a table

39:14

Part 19: Saving several text classification models & extracting performance metrics in a table

Part 18: Tuning Sigmoid SVM for text classification

32:36

Part 18: Tuning Sigmoid SVM for text classification

Part 17: Tuning Support Vector Machines for biomedical text classification in R

36:22

Part 17: Tuning Support Vector Machines for biomedical text classification in R

Part 16: Support vector machines for text classification using the e1071 R package

18:49

Part 16: Support vector machines for text classification using the e1071 R package

Part 15: training SVM text classification model using quanteda text models in R

26:02

Part 15: training SVM text classification model using quanteda text models in R

Part 14: Support Vector Machines

18:38

Part 14: Support Vector Machines