Name: Machine Learning with caret in R
Rating: 5 (3 reviews)

Module 1: Regression models: fitting them and evaluating their performanceModule 2: Classification models: fitting them and evaluating their performance 1. Logistic regression on sonar 2. Why a train/test split? 3. Try a 60/40 split 4. Fit a logistic regression model 5. Confusion matrix 6. Confusion matrix takeaways 7. Calculate a confusion matrix 8. Calculating accuracy 9. Calculating true positive rate 10. Calculating true negative rate 11. Class probabilities and predictions 12. Probabilities and classes 13. Try another threshold 14. From probabilites to confusion matrix 15. Introducing the ROC curve 16. What's the value of a ROC curve? 17. Plot an ROC curve 18. Area under the curve (AUC) 19. Model, ROC, and AUC 20. Customizing trainControl 21. Using custom trainControlModule 3: Tuning model parameters to improve performance 1. Random forests and wine 2. Random forests vs. linear models 3. Fit a random forest 4. Explore a wider model space 5. Advantage of a longer tune length 6. Try a longer tune length 7. Custom tuning grids 8. Advantages of a custom tuning grid 9. Fit a random forest with custom tuning 10. Introducing glmnet 11. Advantage of glmnet 12. Make a custom trainControl 13. Fit glmnet with custom trainControl 14. glmnet with custom tuning grid 15. Why a custom tuning grid? 16. glmnet with custom trainControl and tuning 17. Interpreting glmnet plotsModule 4: Preprocessing your data 1. Median imputation 2. Median imputation vs. omitting rows 3. Apply median imputation 4. KNN imputation 5. Comparing KNN imputation to median imputation 6. Use KNN imputation 7. Compare KNN and median imputation 8. Multiple preprocessing methods 9. Order of operations 10. Combining preprocessing methods 11. Handling low-information predictors 12. Why remove near zero variance predictors? 13. Remove near zero variance predictors 14. preProcess() and nearZeroVar() 15. Fit model on reduced blood-brain data 16. Principle components analysis (PCA) 17. Using PCA as an alternative to nearZeroVar()Module 5: Selecting models: a case study in churn prediction 1. Reusing a trainControl 2. Why reuse a trainControl? 3. Make custom train/test indices 4. Reintroducing glmnet 5. glmnet as a baseline model 6. Fit the baseline model 7. Reintroducing random forest 8. Random forest drawback 9. Random forest with custom trainControl 10. Comparing models 11. Matching train/test indices 12. Create a resamples object 13. More on resamples 14. Create a box-and-whisker plot 15. Create a scatterplot 16. Ensembling models 17. Summary

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Machine learning is the study and application of algorithms that can learn data and make predictions. It is used in all aspects of our lives, including search results and self-driving cars. This is also one the fastest-growing areas of data science research. This course covers the basics of machine learning. This course teaches you how to create, evaluate, tune, and improve predictive models. This course uses the popular R package, which provides a consistent interface to all R's most powerful machine learning tools.

Zachary Deane-Mayer

Max Kuhn

Learn "Machine Learning with caret in R" "Certification" by "DataCamp" and upskill your career by acquiring skills like Random Forests,Machine Learning Algorithms etc with Careervira.

Machine Learning with caret in R

Machine Learning with caret in R

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Zachary Deane-Mayer

Max Kuhn

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₹1,093