Udemy - Machine Learning Basics: Building a Regression model in R

Files:

• 1. Welcome to the course!.mp4 (15.4 MB)
• 1. Welcome to the course!.vtt (3.2 KB)
• 2. Course contents.mp4 (47.0 MB)
• 2. Course contents.vtt (9.4 KB)
• 2.1 00_Introduction_01.pdf.pdf (791.5 KB)

• 1. Types of Data.mp4 (25.9 MB)
• 1. Types of Data.vtt (4.3 KB)
• 1.1 01_01_Lecture_TypesOfData.pdf.pdf (177.7 KB)
• 2. Types of Statistics.mp4 (13.2 MB)
• 2. Types of Statistics.vtt (2.7 KB)
• 2.1 01_02_Lecture_TypesOfStatistics.pdf.pdf (171.7 KB)
• 3. Describing the data graphically.mp4 (82.2 MB)
• 3. Describing the data graphically.vtt (11.3 KB)
• 3.1 01_03_Lecture_DataSummaryandGraph.pdf.pdf (317.9 KB)
• 4. Measures of Centers.mp4 (45.7 MB)
• 4. Measures of Centers.vtt (6.4 KB)
• 4.1 01_04_Lecture_Centers.pdf.pdf (313.0 KB)
• 5. Practice Exercise 1.html (0.3 KB)
• 5.1 Exercise 1.pdf.pdf (553.8 KB)
• 6. Measures of Dispersion.mp4 (28.4 MB)
• 6. Measures of Dispersion.vtt (4.7 KB)
• 6.1 01_05_Lecture_Dispersion.pdf.pdf (210.6 KB)
• 7. Practice Exercise 2.html (0.3 KB)
• 7.1 Exercise 2.pdf.pdf (469.9 KB)

• 1. Installing R and R studio.mp4 (40.8 MB)
• 1. Installing R and R studio.vtt (6.6 KB)
• 2. Basics of R and R studio.mp4 (48.2 MB)
• 2. Basics of R and R studio.vtt (12.8 KB)
• 3. Packages in R.mp4 (98.7 MB)
• 3. Packages in R.vtt (12.9 KB)
• 4. Inputting data part 1 Inbuilt datasets of R.mp4 (46.2 MB)
• 4. Inputting data part 1 Inbuilt datasets of R.vtt (4.9 KB)
• 5. Inputting data part 2 Manual data entry.mp4 (30.9 MB)
• 5. Inputting data part 2 Manual data entry.vtt (3.3 KB)
• 6. Inputting data part 3 Importing from CSV or Text files.mp4 (69.1 MB)
• 6. Inputting data part 3 Importing from CSV or Text files.vtt (7.5 KB)
• 7. Creating Barplots in R.mp4 (117.5 MB)
• 7. Creating Barplots in R.vtt (16.3 KB)
• 8. Creating Histograms in R.mp4 (51.5 MB)
• 8. Creating Histograms in R.vtt (6.8 KB)

• 1. Introduction to Machine Learning.mp4 (123.9 MB)
• 1. Introduction to Machine Learning.vtt (21.0 KB)
• 1.1 Lecture_machineLearning.pdf.pdf (991.6 KB)
• 2. Building a Machine Learning model.mp4 (45.3 MB)
• 2. Building a Machine Learning model.vtt (11.5 KB)
• 2.1 Lecture_machineLearning.pdf.pdf (991.6 KB)
• 3. Introduction to Machine learning quiz.html (0.2 KB)

• 1. Gathering Business Knowledge.mp4 (25.1 MB)
• 1. Gathering Business Knowledge.vtt (3.4 KB)
• 1.1 03_01_PDE_Business_knowledge.pdf.pdf (153.9 KB)
• 10. Outlier Treatment in R.mp4 (38.0 MB)
• 10. Outlier Treatment in R.vtt (3.8 KB)
• 11. Project Exercise 3.html (0.2 KB)
• 12. Missing Value imputation.mp4 (27.6 MB)
• 12. Missing Value imputation.vtt (3.6 KB)
• 12.1 04_05_PDE_Missing_value.pdf.pdf (315.7 KB)
• 13. Missing Value imputation in R.mp4 (31.8 MB)
• 13. Missing Value imputation in R.vtt (3.1 KB)
• 14. Project Exercise 4.html (0.2 KB)
• 15. Seasonality in Data.mp4 (20.9 MB)
• 15. Seasonality in Data.vtt (3.3 KB)
• 15.1 04_07_PDE_Seasonality.pdf.pdf (364.1 KB)
• 16. Bi-variate Analysis and Variable Transformation.mp4 (113.8 MB)
• 16. Bi-variate Analysis and Variable Transformation.vtt (16.1 KB)
• 16.1 04_07_Variable_Transformation.pdf.pdf (422.8 KB)
• 17. Variable transformation in R.mp4 (67.9 MB)
• 17. Variable transformation in R.vtt (8.0 KB)
• 18. Project Exercise 5.html (0.3 KB)
• 19. Non Usable Variables.mp4 (24.0 MB)
• 19. Non Usable Variables.vtt (2.0 MB)
• 19.1 04_08_PDE_Non_Usable_var.pdf.pdf (138.3 KB)
• 2. Data Exploration.mp4 (23.4 MB)
• 2. Data Exploration.vtt (3.2 KB)
• 2.1 03_02_PDE_Data_exploration.pdf.pdf (322.9 KB)
• 20. Dummy variable creation Handling qualitative data.mp4 (40.6 MB)
• 20. Dummy variable creation Handling qualitative data.vtt (4.3 KB)
• 20.1 04_11_Dummy_Var.pdf.pdf (163.0 KB)
• 21. Dummy variable creation in R.mp4 (52.3 MB)
• 21. Dummy variable creation in R.vtt (4.5 KB)
• 22. Project Exercise 6.html (0.2 KB)
• 23. Correlation Matrix and cause-effect relationship.mp4 (81.3 MB)
• 23. Correlation Matrix and cause-effect relationship.vtt (9.7 KB)
• 23.1 04_10_Correlation.pdf.pdf (256.9 KB)
• 24. Correlation Matrix in R.mp4 (95.0 MB)
• 24. Correlation Matrix in R.vtt (8.1 KB)
• 25. Project Exercise 7.html (0.3 KB)
• 3. The Data and the Data Dictionary.mp4 (78.6 MB)
• 3. The Data and the Data Dictionary.vtt (6.9 KB)
• 3.1 House_Price.csv.csv (53.5 KB)
• 3.2 03_03_PDE_Raw_Data_Analysis_Uni.pdf.pdf (332.0 KB)
• 4. Importing the dataset into R.mp4 (16.0 MB)
• 4. Importing the dataset into R.vtt (2.3 KB)
• 4.1 House_Price.csv.csv (53.5 KB)
• 5. Project Exercise 1.html (0.4 KB)
• 5.1 Movie_collection_train.csv.csv (43.3 KB)
• 6. Univariate Analysis and EDD.mp4 (27.3 MB)
• 6. Univariate Analysis and EDD.vtt (3.1 KB)
• 6.1 03_04_PDE_Univariate_Analysis_Uni.pdf.pdf (333.4 KB)
• 7. EDD in R.mp4 (112.3 MB)
• 7. EDD in R.vtt (10.1 KB)
• 8. Project Exercise 2.html (0.2 KB)
• 9. Outlier Treatment.mp4 (27.8 MB)
• 9. Outlier Treatment.vtt (4.0 KB)
• 9.1 04_06_PDE_Outlier_Treatment.pdf.pdf (355.1 KB)

• 1. The problem statement.mp4 (10.7 MB)
• 1. The problem statement.vtt (1.4 KB)
• 1.1 05_01_Intro.pdf.pdf (239.3 KB)
• 10. Multiple Linear Regression in R.mp4 (73.1 MB)
• 10. Multiple Linear Regression in R.vtt (7.1 KB)
• 11. Project Exercise 9.html (0.3 KB)
• 12. Test-Train split.mp4 (49.2 MB)
• 12. Test-Train split.vtt (9.0 KB)
• 12.1 05_12_Test_Train.pdf.pdf (238.8 KB)
• 13. Bias Variance trade-off.mp4 (29.6 MB)
• 13. Bias Variance trade-off.vtt (5.7 KB)
• 13.1 05_13_Bias_Var_tradeoff.pdf.pdf (202.4 KB)
• 14. Test-Train Split in R.mp4 (91.1 MB)
• 14. Test-Train Split in R.vtt (7.3 KB)
• 15. Linear models other than OLS.mp4 (19.2 MB)
• 15. Linear models other than OLS.vtt (3.9 KB)
• 15.1 05_09_Other_lin_model.pdf.pdf (156.5 KB)
• 16. Subset Selection techniques.mp4 (87.1 MB)
• 16. Subset Selection techniques.vtt (11.2 KB)
• 16.1 05_10_Subset_Selection.pdf.pdf (198.5 KB)
• 17. Subset selection in R.mp4 (76.6 MB)
• 17. Subset selection in R.vtt (6.7 KB)
• 18. Project Exercise 10.html (0.2 KB)
• 19. Shrinkage methods - Ridge Regression and The Lasso.mp4 (38.7 MB)
• 19. Shrinkage methods - Ridge Regression and The Lasso.vtt (7.2 KB)
• 19.1 05_11_Shrinkage_methods.pdf.pdf (188.1 KB)
• 2. Basic equations and Ordinary Least Squared (OLS) method.mp4 (50.2 MB)
• 2. Basic equations and Ordinary Least Squared (OLS) method.vtt (8.7 KB)
• 2.1 05_02_Simple_lin_reg.pdf.pdf (284.8 KB)
• 20. Ridge regression and Lasso in R.mp4 (124.2 MB)
• 20. Ridge regression and Lasso in R.vtt (9.9 KB)
• 21. Project Exercise 11.html (0.4 KB)
• 22. Final Project Exercise.html (0.3 KB)
• 22.1 Movie_collection_test.csv.csv (11.7 KB)
• 23. Course Conclusion.html (1.7 KB)
• 3. Assessing Accuracy of predicted coefficients.mp4 (104.4 MB)
• 3. Assessing Accuracy of predicted coefficients.vtt (14.0 KB)
• 3.1 05_03_Simple_lin_reg_Accuracy.pdf.pdf (332.7 KB)
• 4. Assessing Model Accuracy - RSE and R squared.mp4 (49.7 MB)
• 4. Assessing Model Accuracy - RSE and R squared.vtt (7.1 KB)
• 4.1 05_03_Simple_lin_reg_Accuracy.pdf.pdf (332.7 KB)
• 5. Simple Linear Regression in R.mp4 (50.6 MB)
• 5. Simple Linear Regression in R.vtt (7.1 KB)
• 6. Project Exercise 8.html (0.3 KB)
• 7. Multiple Linear Regression.mp4 (38.9 MB)
• 7. Multiple Linear Regression.vtt (5.1 KB)
• 7.1 05_04_Multiple_lin_reg.pdf.pdf (219.8 KB)
• 8. The F - statistic.mp4 (64.2 MB)
• 8. The F - statistic.vtt (8.0 KB)
• 8.1 05_05_F_stat.pdf.pdf (328.5 KB)
• 9. Interpreting result for categorical Variable.mp4 (27.2 MB)
• 9. Interpreting result for categorical Variable.vtt (4.7 KB)
• 9.1 05_06_Cat_var.pdf.pdf (155.5 KB)

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