Udemy - Complete Machine Learning with R Studio - ML for 2021 [Gi...

Files:

• [GigaCourse.Com].url (0.0 KB)

• 001 Introduction.mp4 (21.2 MB)
• 002 Course Resources.html (1.2 KB)

• 001 Installing R and R studio.mp4 (40.8 MB)
• 002 This is a milestone!.mp4 (20.7 MB)
• 003 Basics of R and R studio.mp4 (48.0 MB)
• 004 Packages in R.mp4 (98.5 MB)
• 005 Inputting data part 1_ Inbuilt datasets of R.mp4 (46.1 MB)
• 006 Inputting data part 2_ Manual data entry.mp4 (30.8 MB)
• 007 Inputting data part 3_ Importing from CSV or Text files.mp4 (69.0 MB)
• 008 Creating Barplots in R.mp4 (117.2 MB)
• 009 Creating Histograms in R.mp4 (51.3 MB)
• 009 Customer.csv (64.0 KB)
• 009 Product.txt (137.7 KB)

• 001 Types of Data.mp4 (21.8 MB)
• 002 Types of Statistics.mp4 (10.9 MB)
• 003 Describing the data graphically.mp4 (65.4 MB)
• 004 Measures of Centers.mp4 (38.5 MB)
• 005 Measures of Dispersion.mp4 (22.8 MB)

• 001 Introduction to Machine Learning.mp4 (123.3 MB)
• 002 Building a Machine Learning Model.mp4 (44.9 MB)

• 001 Gathering Business Knowledge.mp4 (25.0 MB)
• 002 Data Exploration.mp4 (23.3 MB)
• 003 The Data and the Data Dictionary.mp4 (78.3 MB)
• 004 Importing the dataset into R.mp4 (15.9 MB)
• 005 Univariate Analysis and EDD.mp4 (27.2 MB)
• 006 EDD in R.mp4 (112.0 MB)
• 007 Outlier Treatment.mp4 (27.7 MB)
• 008 Outlier Treatment in R.mp4 (37.8 MB)
• 009 Missing Value imputation.mp4 (27.4 MB)
• 010 Missing Value imputation in R.mp4 (31.7 MB)
• 011 Seasonality in Data.mp4 (20.8 MB)
• 012 Bi-variate Analysis and Variable Transformation.mp4 (113.1 MB)
• 013 Variable transformation in R.mp4 (67.6 MB)
• 014 Non Usable Variables.mp4 (23.7 MB)
• 015 Dummy variable creation_ Handling qualitative data.mp4 (40.5 MB)
• 016 Dummy variable creation in R.mp4 (52.2 MB)
• 017 Correlation Matrix and cause-effect relationship.mp4 (80.8 MB)
• 018 Correlation Matrix in R.mp4 (94.9 MB)
• [GigaCourse.Com].url (0.0 KB)

• 001 The problem statement.mp4 (10.6 MB)
• 002 Basic equations and Ordinary Least Squared (OLS) method.mp4 (49.9 MB)
• 003 Assessing Accuracy of predicted coefficients.mp4 (103.9 MB)
• 004 Assessing Model Accuracy - RSE and R squared.mp4 (49.5 MB)
• 005 Simple Linear Regression in R.mp4 (50.5 MB)
• 006 Multiple Linear Regression.mp4 (38.7 MB)
• 007 The F - statistic.mp4 (63.8 MB)
• 008 Interpreting result for categorical Variable.mp4 (26.9 MB)
• 009 Multiple Linear Regression in R.mp4 (72.8 MB)
• 010 Test-Train split.mp4 (48.8 MB)
• 011 Bias Variance trade-off.mp4 (29.4 MB)
• 012 More about test-train split.html (1.4 KB)
• 013 Test-Train Split in R.mp4 (90.9 MB)

• 001 Linear models other than OLS.mp4 (19.0 MB)
• 002 Subset Selection techniques.mp4 (86.7 MB)
• 003 Subset selection in R.mp4 (76.6 MB)
• 004 Shrinkage methods - Ridge Regression and The Lasso.mp4 (38.4 MB)
• 005 Ridge regression and Lasso in R.mp4 (124.0 MB)

• 001 The Data and the Data Dictionary.mp4 (87.4 MB)
• 002 Importing the dataset into R.mp4 (16.3 MB)
• 003 EDD in R.mp4 (77.8 MB)
• 004 Outlier Treatment in R.mp4 (31.2 MB)
• 005 Missing Value imputation in R.mp4 (23.4 MB)
• 006 Variable transformation in R.mp4 (46.5 MB)
• 007 Dummy variable creation in R.mp4 (52.5 MB)

• 001 Three Classifiers and the problem statement.mp4 (22.8 MB)
• 002 Why can't we use Linear Regression_.mp4 (20.2 MB)

• 001 Logistic Regression.mp4 (38.8 MB)
• 002 Training a Simple Logistic model in R.mp4 (31.0 MB)
• 003 Results of Simple Logistic Regression.mp4 (30.9 MB)
• 004 Logistic with multiple predictors.mp4 (9.9 MB)
• 005 Training multiple predictor Logistic model in R.mp4 (18.3 MB)
• 006 Confusion Matrix.mp4 (26.6 MB)
• 007 Evaluating Model performance.mp4 (42.5 MB)
• 008 Predicting probabilities, assigning classes and making Confusion Matrix in R.mp4 (66.1 MB)
• [GigaCourse.Com].url (0.0 KB)

• 001 Linear Discriminant Analysis.mp4 (48.4 MB)
• 002 Linear Discriminant Analysis in R.mp4 (89.5 MB)

• 001 Test-Train Split.mp4 (45.4 MB)
• 002 Test-Train Split in R.mp4 (90.2 MB)
• 003 K-Nearest Neighbors classifier.mp4 (83.3 MB)
• 004 K-Nearest Neighbors in R.mp4 (79.6 MB)

• 001 Understanding the results of classification models.mp4 (45.8 MB)
• 002 Summary of the three models.mp4 (25.1 MB)

• 001 Basics of Decision Trees.mp4 (50.6 MB)
• 002 Understanding a Regression Tree.mp4 (52.2 MB)
• 003 The stopping criteria for controlling tree growth.mp4 (16.5 MB)
• 004 The Data set for this part.mp4 (42.0 MB)
• 005 Course resources_ Notes and Datasets.html (1.0 KB)
• 006 Importing the Data set into R.mp4 (51.8 MB)
• 007 Splitting Data into Test and Train Set in R.mp4 (52.6 MB)
• 008 Building a Regression Tree in R.mp4 (121.9 MB)
• 009 Pruning a tree.mp4 (22.2 MB)
• 010 Pruning a Tree in R.mp4 (97.0 MB)
Files
• 00_Intro.pdf (334.9 KB)
• 01_basics.pdf (166.0 KB)
• 02_Decision Tree.pdf (205.8 KB)
• 03_Concepts.pdf (221.7 KB)
• 04_Stop_condition.pdf (154.8 KB)
• 05_Prune.pdf (228.5 KB)
• 06_Decision Tree - Class.pdf (209.2 KB)
• 07_Bagging.pdf (303.7 KB)
• 08_Random_Forest.pdf (168.4 KB)
• 09_Boosting.pdf (178.0 KB)
• 10_Adv_disadv.pdf (145.5 KB)
• Movie_classification.csv (54.3 KB)
• Movie_regression.csv (53.3 KB)
• tree_R.R (7.5 KB)
15 Simple Classification Tree
• 001 Classification Trees.mp4 (33.0 MB)
• 002 The Data set for Classification problem.mp4 (21.9 MB)
• 003 Building a classification Tree in R.mp4 (100.1 MB)
• 004 Advantages and Disadvantages of Decision Trees.mp4 (7.8 MB)
16 Ensemble technique 1 - Bagging
• 001 Bagging.mp4 (32.3 MB)
• 002 Bagging in R.mp4 (69.3 MB)
17 Ensemble technique 2 - Random Forest
• 001 Random Forest technique.mp4 (21.4 MB)
• 002 Random Forest in R.mp4 (37.4 MB)
• [GigaCourse.Com].url (0.0 KB)
18 Ensemble technique 3 - GBM, AdaBoost and XGBoost
• 001 Boosting techniques.mp4 (34.4 MB)
• 002 Gradient Boosting in R.mp4 (78.6 MB)
• 003 AdaBoosting in R.mp4 (103.0 MB)
• 004 XGBoosting in R.mp4 (186.5 MB)
19 Maximum Margin Classifier
• 001 Content flow.mp4 (9.8 MB)
• 002 The Concept of a Hyperplane.mp4 (35.3 MB)
• 003 Maximum Margin Classifier.mp4 (26.2 MB)
• 004 Limitations of Maximum Margin Classifier.mp4 (12.5 MB)
20 Support Vector Classifier
• 001 Support Vector classifiers.mp4 (64.1 MB)
• 002 Limitations of Support Vector Classifiers.mp4 (13.0 MB)
21 Support Vector Machines
• 001 Kernel Based Support Vector Machines.mp4 (45.7 MB)
22 Creating Support Vector Machine Model in R
• 001 The Data set for the Classification problem.mp4 (22.0 MB)
• 002 Course resources_ Notes and Datasets.html (0.9 KB)
• 003 Importing Data into R.mp4 (65.3 MB)
• 004 Test-Train Split.mp4 (59.4 MB)
• 005 Classification SVM model using Linear Kernel.mp4 (166.9 MB)
• 006 Hyperparameter Tuning for Linear Kernel.mp4 (70.4 MB)
• 007 Polynomial Kernel with Hyperparameter Tuning.mp4 (98.7 MB)
• 008 Radial Kernel with Hyperparameter Tuning.mp4 (67.4 MB)
• 009 The Data set for the Regression problem.mp4 (41.8 MB)
• 010 SVM based Regression Model in R.mp4 (124.0 MB)
Files
• 00000_Intro.pdf (334.9 KB)
• 01_SVM_flow.pdf (143.9 KB)
• 02_Max_Mar_Class.pdf (287.9 KB)
• 03_Max_Mar_Class_LIMIT.pdf (328.7 KB)
• 04_support_v_class.pdf (189.0 KB)
• 05_Support_vec_class_LIMIT.pdf (198.7 KB)
• 06_SVM.pdf (360.4 KB)
• Movie_classification.csv (54.3 KB)
• Movie_regression.csv (53.3 KB)
• SVM_R.R (3.0 KB)
• [GigaCourse.Com].url (0.0 KB)
23 Conclusion
• 001 The final milestone!.mp4 (11.9 MB)
• 002 Congratulations & About your certificate.html (2.7 KB)
• [GigaCourse.Com].url (0.0 KB)