GigaCourse Com Udemy - Complete Machine Learning with R Studio - ML for 2021

1. 01 Welcome to the course/001 Introduction.mp421.18MB
2. 02 Setting up R Studio and R crash course/001 Installing R and R studio.mp440.77MB
3. 02 Setting up R Studio and R crash course/002 This is a milestone!.mp420.69MB
4. 02 Setting up R Studio and R crash course/003 Basics of R and R studio.mp447.95MB
5. 02 Setting up R Studio and R crash course/004 Packages in R.mp498.48MB
6. 02 Setting up R Studio and R crash course/005 Inputting data part 1_ Inbuilt datasets of R.mp446.14MB
7. 02 Setting up R Studio and R crash course/006 Inputting data part 2_ Manual data entry.mp430.78MB
8. 02 Setting up R Studio and R crash course/007 Inputting data part 3_ Importing from CSV or Text files.mp468.96MB
9. 02 Setting up R Studio and R crash course/008 Creating Barplots in R.mp4117.22MB
10. 02 Setting up R Studio and R crash course/009 Creating Histograms in R.mp451.34MB
11. 03 Basics of Statistics/001 Types of Data.mp421.75MB
12. 03 Basics of Statistics/002 Types of Statistics.mp410.93MB
13. 03 Basics of Statistics/003 Describing the data graphically.mp465.37MB
14. 03 Basics of Statistics/004 Measures of Centers.mp438.54MB
15. 03 Basics of Statistics/005 Measures of Dispersion.mp422.85MB
16. 04 Intorduction to Machine Learning/001 Introduction to Machine Learning.mp4123.27MB
17. 04 Intorduction to Machine Learning/002 Building a Machine Learning Model.mp444.92MB
18. 05 Data Preprocessing for Regression Analysis/001 Gathering Business Knowledge.mp425.01MB
19. 05 Data Preprocessing for Regression Analysis/002 Data Exploration.mp423.3MB
20. 05 Data Preprocessing for Regression Analysis/003 The Data and the Data Dictionary.mp478.32MB
21. 05 Data Preprocessing for Regression Analysis/004 Importing the dataset into R.mp415.93MB
22. 05 Data Preprocessing for Regression Analysis/005 Univariate Analysis and EDD.mp427.2MB
23. 05 Data Preprocessing for Regression Analysis/006 EDD in R.mp4111.99MB
24. 05 Data Preprocessing for Regression Analysis/007 Outlier Treatment.mp427.67MB
25. 05 Data Preprocessing for Regression Analysis/008 Outlier Treatment in R.mp437.83MB
26. 05 Data Preprocessing for Regression Analysis/009 Missing Value imputation.mp427.37MB
27. 05 Data Preprocessing for Regression Analysis/010 Missing Value imputation in R.mp431.7MB
28. 05 Data Preprocessing for Regression Analysis/011 Seasonality in Data.mp420.78MB
29. 05 Data Preprocessing for Regression Analysis/012 Bi-variate Analysis and Variable Transformation.mp4113.07MB
30. 05 Data Preprocessing for Regression Analysis/013 Variable transformation in R.mp467.58MB
31. 05 Data Preprocessing for Regression Analysis/014 Non Usable Variables.mp423.73MB
32. 05 Data Preprocessing for Regression Analysis/015 Dummy variable creation_ Handling qualitative data.mp440.53MB
33. 05 Data Preprocessing for Regression Analysis/016 Dummy variable creation in R.mp452.22MB
34. 05 Data Preprocessing for Regression Analysis/017 Correlation Matrix and cause-effect relationship.mp480.84MB
35. 05 Data Preprocessing for Regression Analysis/018 Correlation Matrix in R.mp494.94MB
36. 06 Linear Regression Model/001 The problem statement.mp410.64MB
37. 06 Linear Regression Model/002 Basic equations and Ordinary Least Squared (OLS) method.mp449.92MB
38. 06 Linear Regression Model/003 Assessing Accuracy of predicted coefficients.mp4103.87MB
39. 06 Linear Regression Model/004 Assessing Model Accuracy - RSE and R squared.mp449.52MB
40. 06 Linear Regression Model/005 Simple Linear Regression in R.mp450.46MB
41. 06 Linear Regression Model/006 Multiple Linear Regression.mp438.7MB
42. 06 Linear Regression Model/007 The F - statistic.mp463.83MB
43. 06 Linear Regression Model/008 Interpreting result for categorical Variable.mp426.94MB
44. 06 Linear Regression Model/009 Multiple Linear Regression in R.mp472.82MB
45. 06 Linear Regression Model/010 Test-Train split.mp448.75MB
46. 06 Linear Regression Model/011 Bias Variance trade-off.mp429.38MB
47. 06 Linear Regression Model/013 Test-Train Split in R.mp490.93MB
48. 07 Regression models other than OLS/001 Linear models other than OLS.mp419.02MB
49. 07 Regression models other than OLS/002 Subset Selection techniques.mp486.66MB
50. 07 Regression models other than OLS/003 Subset selection in R.mp476.59MB
51. 07 Regression models other than OLS/004 Shrinkage methods - Ridge Regression and The Lasso.mp438.44MB
52. 07 Regression models other than OLS/005 Ridge regression and Lasso in R.mp4124.01MB
53. 08 Classification Models_ Data Preparation/001 The Data and the Data Dictionary.mp487.44MB
54. 08 Classification Models_ Data Preparation/002 Importing the dataset into R.mp416.31MB
55. 08 Classification Models_ Data Preparation/003 EDD in R.mp477.81MB
56. 08 Classification Models_ Data Preparation/004 Outlier Treatment in R.mp431.24MB
57. 08 Classification Models_ Data Preparation/005 Missing Value imputation in R.mp423.41MB
58. 08 Classification Models_ Data Preparation/006 Variable transformation in R.mp446.48MB
59. 08 Classification Models_ Data Preparation/007 Dummy variable creation in R.mp452.45MB
60. 09 The Three classification models/001 Three Classifiers and the problem statement.mp422.8MB
61. 09 The Three classification models/002 Why can't we use Linear Regression_.mp420.24MB
62. 10 Logistic Regression/001 Logistic Regression.mp438.81MB
63. 10 Logistic Regression/002 Training a Simple Logistic model in R.mp431MB
64. 10 Logistic Regression/003 Results of Simple Logistic Regression.mp430.9MB
65. 10 Logistic Regression/004 Logistic with multiple predictors.mp49.94MB
66. 10 Logistic Regression/005 Training multiple predictor Logistic model in R.mp418.28MB
67. 10 Logistic Regression/006 Confusion Matrix.mp426.55MB
68. 10 Logistic Regression/007 Evaluating Model performance.mp442.51MB
69. 10 Logistic Regression/008 Predicting probabilities, assigning classes and making Confusion Matrix in R.mp466.07MB
70. 11 Linear Discriminant Analysis/001 Linear Discriminant Analysis.mp448.38MB
71. 11 Linear Discriminant Analysis/002 Linear Discriminant Analysis in R.mp489.5MB
72. 12 K-Nearest Neighbors/001 Test-Train Split.mp445.37MB
73. 12 K-Nearest Neighbors/002 Test-Train Split in R.mp490.16MB
74. 12 K-Nearest Neighbors/003 K-Nearest Neighbors classifier.mp483.26MB
75. 12 K-Nearest Neighbors/004 K-Nearest Neighbors in R.mp479.65MB
76. 13 Comparing results from 3 models/001 Understanding the results of classification models.mp445.79MB
77. 13 Comparing results from 3 models/002 Summary of the three models.mp425.12MB
78. 14 Simple Decision Trees/001 Basics of Decision Trees.mp450.55MB
79. 14 Simple Decision Trees/002 Understanding a Regression Tree.mp452.16MB
80. 14 Simple Decision Trees/003 The stopping criteria for controlling tree growth.mp416.49MB
81. 14 Simple Decision Trees/004 The Data set for this part.mp441.95MB
82. 14 Simple Decision Trees/006 Importing the Data set into R.mp451.83MB
83. 14 Simple Decision Trees/007 Splitting Data into Test and Train Set in R.mp452.57MB
84. 14 Simple Decision Trees/008 Building a Regression Tree in R.mp4121.87MB
85. 14 Simple Decision Trees/009 Pruning a tree.mp422.22MB
86. 14 Simple Decision Trees/010 Pruning a Tree in R.mp496.96MB
87. 15 Simple Classification Tree/001 Classification Trees.mp433.04MB
88. 15 Simple Classification Tree/002 The Data set for Classification problem.mp421.94MB
89. 15 Simple Classification Tree/003 Building a classification Tree in R.mp4100.09MB
90. 15 Simple Classification Tree/004 Advantages and Disadvantages of Decision Trees.mp47.75MB
91. 16 Ensemble technique 1 - Bagging/001 Bagging.mp432.33MB
92. 16 Ensemble technique 1 - Bagging/002 Bagging in R.mp469.33MB
93. 17 Ensemble technique 2 - Random Forest/001 Random Forest technique.mp421.42MB
94. 17 Ensemble technique 2 - Random Forest/002 Random Forest in R.mp437.43MB
95. 18 Ensemble technique 3 - GBM, AdaBoost and XGBoost/001 Boosting techniques.mp434.36MB
96. 18 Ensemble technique 3 - GBM, AdaBoost and XGBoost/002 Gradient Boosting in R.mp478.56MB
97. 18 Ensemble technique 3 - GBM, AdaBoost and XGBoost/003 AdaBoosting in R.mp4102.98MB
98. 18 Ensemble technique 3 - GBM, AdaBoost and XGBoost/004 XGBoosting in R.mp4186.45MB
99. 19 Maximum Margin Classifier/001 Content flow.mp49.77MB
100. 19 Maximum Margin Classifier/002 The Concept of a Hyperplane.mp435.32MB
101. 19 Maximum Margin Classifier/003 Maximum Margin Classifier.mp426.16MB
102. 19 Maximum Margin Classifier/004 Limitations of Maximum Margin Classifier.mp412.51MB
103. 20 Support Vector Classifier/001 Support Vector classifiers.mp464.12MB
104. 20 Support Vector Classifier/002 Limitations of Support Vector Classifiers.mp412.97MB
105. 21 Support Vector Machines/001 Kernel Based Support Vector Machines.mp445.7MB
106. 22 Creating Support Vector Machine Model in R/001 The Data set for the Classification problem.mp421.98MB
107. 22 Creating Support Vector Machine Model in R/003 Importing Data into R.mp465.32MB
108. 22 Creating Support Vector Machine Model in R/004 Test-Train Split.mp459.36MB
109. 22 Creating Support Vector Machine Model in R/005 Classification SVM model using Linear Kernel.mp4166.93MB
110. 22 Creating Support Vector Machine Model in R/006 Hyperparameter Tuning for Linear Kernel.mp470.43MB
111. 22 Creating Support Vector Machine Model in R/007 Polynomial Kernel with Hyperparameter Tuning.mp498.66MB
112. 22 Creating Support Vector Machine Model in R/008 Radial Kernel with Hyperparameter Tuning.mp467.37MB
113. 22 Creating Support Vector Machine Model in R/009 The Data set for the Regression problem.mp441.76MB
114. 22 Creating Support Vector Machine Model in R/010 SVM based Regression Model in R.mp4124.01MB
115. 23 Conclusion/001 The final milestone!.mp411.86MB