DesireCourse Net Udemy - Machine Learning for Beginners Linear Regression model in R

1. 1. Introduction/1. Welcome to the course!.mp415.41MB
2. 1. Introduction/3. Course contents.mp447.05MB
3. 2. Basics of Statistics/1. Types of Data.mp425.87MB
4. 2. Basics of Statistics/2. Types of Statistics.mp413.24MB
5. 2. Basics of Statistics/3. Describing the data graphically.mp482.16MB
6. 2. Basics of Statistics/4. Measures of Centers.mp445.68MB
7. 2. Basics of Statistics/6. Measures of Dispersion.mp428.37MB
8. 3. Getting started with R and R studio/1. Installing R and R studio.mp440.84MB
9. 3. Getting started with R and R studio/2. Basics of R and R studio.mp448.19MB
10. 3. Getting started with R and R studio/3. Packages in R.mp498.67MB
11. 3. Getting started with R and R studio/4. Inputting data part 1 Inbuilt datasets of R.mp446.15MB
12. 3. Getting started with R and R studio/5. Inputting data part 2 Manual data entry.mp430.88MB
13. 3. Getting started with R and R studio/6. Inputting data part 3 Importing from CSV or Text files.mp469.15MB
14. 3. Getting started with R and R studio/7. Creating Barplots in R.mp4117.54MB
15. 3. Getting started with R and R studio/8. Creating Histograms in R.mp451.51MB
16. 4. Introduction to Machine Learning/1. Introduction to Machine Learning.mp4123.89MB
17. 4. Introduction to Machine Learning/2. Building a Machine Learning model.mp445.27MB
18. 5. Data Preprocessing/1. Gathering Business Knowledge.mp425.12MB
19. 5. Data Preprocessing/10. Outlier Treatment in R.mp437.97MB
20. 5. Data Preprocessing/12. Missing Value imputation.mp427.56MB
21. 5. Data Preprocessing/13. Missing Value imputation in R.mp431.77MB
22. 5. Data Preprocessing/15. Seasonality in Data.mp420.89MB
23. 5. Data Preprocessing/16. Bi-variate Analysis and Variable Transformation.mp4113.76MB
24. 5. Data Preprocessing/17. Variable transformation in R.mp467.85MB
25. 5. Data Preprocessing/19. Non Usable Variables.mp423.95MB
26. 5. Data Preprocessing/2. Data Exploration.mp423.41MB
27. 5. Data Preprocessing/20. Dummy variable creation Handling qualitative data.mp440.61MB
28. 5. Data Preprocessing/21. Dummy variable creation in R.mp452.26MB
29. 5. Data Preprocessing/23. Correlation Matrix and cause-effect relationship.mp481.28MB
30. 5. Data Preprocessing/24. Correlation Matrix in R.mp495.06MB
31. 5. Data Preprocessing/3. The Data and the Data Dictionary.mp478.58MB
32. 5. Data Preprocessing/4. Importing the dataset into R.mp415.98MB
33. 5. Data Preprocessing/6. Univariate Analysis and EDD.mp427.3MB
34. 5. Data Preprocessing/7. EDD in R.mp4112.27MB
35. 5. Data Preprocessing/9. Outlier Treatment.mp427.76MB
36. 6. Linear Regression Model/1. The problem statement.mp410.68MB
37. 6. Linear Regression Model/11. Multiple Linear Regression in R.mp473.1MB
38. 6. Linear Regression Model/14. Test-Train split.mp449.15MB
39. 6. Linear Regression Model/15. Bias Variance trade-off.mp429.58MB
40. 6. Linear Regression Model/16. Test-Train Split in R.mp491.05MB
41. 6. Linear Regression Model/2. Basic equations and Ordinary Least Squared (OLS) method.mp450.23MB
42. 6. Linear Regression Model/3. Assessing Accuracy of predicted coefficients.mp4104.44MB
43. 6. Linear Regression Model/4. Assessing Model Accuracy - RSE and R squared.mp449.73MB
44. 6. Linear Regression Model/5. Simple Linear Regression in R.mp450.6MB
45. 6. Linear Regression Model/7. Multiple Linear Regression.mp438.92MB
46. 6. Linear Regression Model/8. The F - statistic.mp464.16MB
47. 6. Linear Regression Model/9. Interpreting result for categorical Variable.mp427.16MB
48. 7. Regression models other than OLS/1. Linear models other than OLS.mp419.19MB
49. 7. Regression models other than OLS/2. Subset Selection techniques.mp487.11MB
50. 7. Regression models other than OLS/3. Subset selection in R.mp476.62MB
51. 7. Regression models other than OLS/5. Shrinkage methods - Ridge Regression and The Lasso.mp438.67MB
52. 7. Regression models other than OLS/6. Ridge regression and Lasso in R.mp4124.25MB
53. 7. Regression models other than OLS/7. Heteroscedasticity.mp417.72MB