FreeCourseSite com Udemy Complete Machine Learning with Studio ML for 2023

FreeCourseSite com Udemy Complete Machine Learning with Studio ML for 2023

Name	[FreeCourseSite.com] Udemy - Complete Machine Learning with R Studio - ML for 2023	DOWNLOAD Copy Link
Total Size	5.9 GB
Total Files	237
Last Seen	2024-07-22 23:59
Hash	C6D7044BEB36D6EF59890B0FDEA52F71A30C9BAB

/0. Websites you may like/
[CourseClub.Me].url	0.1 KB
[FreeCourseSite.com].url	0.1 KB
[GigaCourse.Com].url	0.0 KB
/1. Welcome to the course/
1. Introduction.mp4	22.2 MB
1. Introduction.srt	3.0 KB
2. Course Resources.html	0.3 KB
/10. Linear Discriminant Analysis/
1. Linear Discriminant Analysis.mp4	50.7 MB
1. Linear Discriminant Analysis.srt	12.6 KB
2. Linear Discriminant Analysis in R.mp4	93.8 MB
2. Linear Discriminant Analysis in R.srt	10.7 KB
/.../0. Websites you may like/
[CourseClub.Me].url	0.1 KB
[FreeCourseSite.com].url	0.1 KB
[GigaCourse.Com].url	0.0 KB
/11. K-Nearest Neighbors/
1. Test-Train Split.mp4	47.6 MB
1. Test-Train Split.srt	11.2 KB
2. Test-Train Split in R.mp4	94.5 MB
2. Test-Train Split in R.srt	10.5 KB
3. K-Nearest Neighbors classifier.mp4	87.3 MB
3. K-Nearest Neighbors classifier.srt	10.6 KB
4. K-Nearest Neighbors in R.mp4	83.5 MB
4. K-Nearest Neighbors in R.srt	9.6 KB
/12. Comparing results from 3 models/
1. Understanding the results of classification models.mp4	48.0 MB
1. Understanding the results of classification models.srt	8.0 KB
2. Summary of the three models.mp4	26.3 MB
2. Summary of the three models.srt	6.4 KB
/13. Simple Decision Trees/
1. Introduction to Decision trees.mp4	46.9 MB
1. Introduction to Decision trees.srt	4.7 KB
10. Pruning a Tree in R.mp4	101.7 MB
10. Pruning a Tree in R.srt	12.1 KB
2. Basics of Decision Trees.mp4	53.0 MB
2. Basics of Decision Trees.srt	13.5 KB
3. Understanding a Regression Tree.mp4	54.7 MB
3. Understanding a Regression Tree.srt	14.3 KB
4. The stopping criteria for controlling tree growth.mp4	17.3 MB
4. The stopping criteria for controlling tree growth.srt	4.4 KB
5. Course resources Notes and Datasets.html	0.1 KB
5.1 Files_Dt_r.zip	2.2 MB
6. Importing the Data set into R.mp4	54.4 MB
6. Importing the Data set into R.srt	9.0 KB
7. Splitting Data into Test and Train Set in R.mp4	55.1 MB
7. Splitting Data into Test and Train Set in R.srt	7.5 KB
8. Building a Regression Tree in R.mp4	127.8 MB
8. Building a Regression Tree in R.srt	19.3 KB
9. Pruning a tree.mp4	23.3 MB
9. Pruning a tree.srt	5.6 KB
/14. Simple Classification Tree/
1. Classification Trees.mp4	34.6 MB
1. Classification Trees.srt	8.3 KB
2. The Data set for Classification problem.mp4	23.0 MB
2. The Data set for Classification problem.srt	2.4 KB
3. Building a classification Tree in R.mp4	105.0 MB
3. Building a classification Tree in R.srt	12.2 KB
4. Advantages and Disadvantages of Decision Trees.mp4	8.1 MB
4. Advantages and Disadvantages of Decision Trees.srt	2.2 KB
/15. Ensemble technique 1 - Bagging/
1. Bagging.mp4	33.9 MB
1. Bagging.srt	7.8 KB
2. Bagging in R.mp4	72.7 MB
2. Bagging in R.srt	8.4 KB
/16. Ensemble technique 2 - Random Forest/
1. Random Forest technique.mp4	22.5 MB
1. Random Forest technique.srt	5.2 KB
2. Random Forest in R.mp4	39.3 MB
2. Random Forest in R.srt	5.7 KB
/17. Ensemble technique 3 - GBM, AdaBoost and XGBoost/
1. Boosting techniques.mp4	36.0 MB
1. Boosting techniques.srt	9.8 KB
2. Gradient Boosting in R.mp4	82.4 MB
2. Gradient Boosting in R.srt	9.8 KB
3. AdaBoosting in R.mp4	108.0 MB
3. AdaBoosting in R.srt	12.5 KB
4. XGBoosting in R.mp4	195.5 MB
4. XGBoosting in R.srt	21.6 KB
/18. Support Vector Machines/
1. Introduction to SVM.mp4	22.7 MB
1. Introduction to SVM.srt	3.2 KB
2. The Concept of a Hyperplane.mp4	37.1 MB
2. The Concept of a Hyperplane.srt	6.4 KB
3. Maximum Margin Classifier.mp4	27.4 MB
3. Maximum Margin Classifier.srt	4.5 KB
4. Limitations of Maximum Margin Classifier.mp4	13.1 MB
4. Limitations of Maximum Margin Classifier.srt	3.2 KB
/19. Support Vector Classifier/
1. Support Vector classifiers.mp4	67.2 MB
1. Support Vector classifiers.srt	12.8 KB
2. Limitations of Support Vector Classifiers.mp4	13.6 MB
2. Limitations of Support Vector Classifiers.srt	1.9 KB
/2. Setting up R Studio and R crash course/
1. Installing R and R studio.mp4	42.8 MB
1. Installing R and R studio.srt	7.5 KB
2. This is a milestone!.mp4	21.7 MB
2. This is a milestone!.srt	4.0 KB
3. Basics of R and R studio.mp4	50.3 MB
3. Basics of R and R studio.srt	14.7 KB
4. Packages in R.mp4	103.3 MB
4. Packages in R.srt	14.9 KB
5. Inputting data part 1 Inbuilt datasets of R.mp4	48.4 MB
5. Inputting data part 1 Inbuilt datasets of R.srt	5.7 KB
6. Inputting data part 2 Manual data entry.mp4	32.3 MB
6. Inputting data part 2 Manual data entry.srt	3.8 KB
7. Inputting data part 3 Importing from CSV or Text files.mp4	72.3 MB
7. Inputting data part 3 Importing from CSV or Text files.srt	8.6 KB
7.1 Customer.csv	65.6 KB
7.2 Product.txt	142.8 KB
8. Creating Barplots in R.mp4	122.9 MB
8. Creating Barplots in R.srt	18.8 KB
9. Creating Histograms in R.mp4	53.8 MB
9. Creating Histograms in R.srt	7.8 KB
/20. Support Vector Machines/
1. Kernel Based Support Vector Machines.mp4	47.9 MB
1. Kernel Based Support Vector Machines.srt	8.7 KB
2. Quiz.html	0.2 KB
/21. Creating Support Vector Machine Model in R/
1. Course resources Notes and Datasets.html	0.1 KB
1.1 Files_svm_r.zip	1.8 MB
2. Importing and preprocessing data.mp4	26.2 MB
2. Importing and preprocessing data.srt	2.8 KB
3. Classification SVM model using Linear Kernel.mp4	175.1 MB
3. Classification SVM model using Linear Kernel.srt	18.8 KB
4. Hyperparameter Tuning for Linear Kernel.mp4	73.9 MB
4. Hyperparameter Tuning for Linear Kernel.srt	7.3 KB
5. Polynomial Kernel with Hyperparameter Tuning.mp4	103.4 MB
5. Polynomial Kernel with Hyperparameter Tuning.srt	12.1 KB
6. Radial Kernel with Hyperparameter Tuning.mp4	70.6 MB
6. Radial Kernel with Hyperparameter Tuning.srt	7.5 KB
7. SVM based Regression Model in R.mp4	130.0 MB
7. SVM based Regression Model in R.srt	13.0 KB
/22. Congratulations & about your certificate/
1. The final milestone!.mp4	12.4 MB
1. The final milestone!.srt	1.8 KB
2. Bonus Lecture.html	2.4 KB
/3. Basics of Statistics/
1. Types of Data.mp4	22.8 MB
1. Types of Data.srt	5.3 KB
2. Types of Statistics.mp4	11.5 MB
2. Types of Statistics.srt	3.4 KB
3. Describing the data graphically.mp4	68.5 MB
3. Describing the data graphically.srt	13.5 KB
4. Measures of Centers.mp4	40.4 MB
4. Measures of Centers.srt	8.3 KB
5. Measures of Dispersion.mp4	24.0 MB
5. Measures of Dispersion.srt	5.4 KB
/4. Intorduction to Machine Learning/
1. Introduction to Machine Learning.mp4	129.3 MB
1. Introduction to Machine Learning.srt	19.8 KB
2. Building a Machine Learning Model.mp4	47.1 MB
2. Building a Machine Learning Model.srt	10.5 KB
3. Quiz Introduction to Machine Learning.html	0.2 KB
/5. Data Preprocessing for Regression Analysis/
1. Gathering Business Knowledge.mp4	15.2 MB
1. Gathering Business Knowledge.srt	3.9 KB
10. Missing Value imputation in R.mp4	33.2 MB
10. Missing Value imputation in R.srt	4.2 KB
11. Seasonality in Data.mp4	21.8 MB
11. Seasonality in Data.srt	4.2 KB
12. Bi-variate Analysis and Variable Transformation.mp4	118.6 MB
12. Bi-variate Analysis and Variable Transformation.srt	20.7 KB
13. Variable transformation in R.mp4	70.9 MB
13. Variable transformation in R.srt	9.5 KB
14. Non Usable Variables.mp4	24.9 MB
14. Non Usable Variables.srt	6.4 KB
15. Dummy variable creation Handling qualitative data.mp4	42.5 MB
15. Dummy variable creation Handling qualitative data.srt	5.7 KB
16. Dummy variable creation in R.mp4	54.8 MB
16. Dummy variable creation in R.srt	6.6 KB
17. Correlation Matrix and cause-effect relationship.mp4	84.8 MB
17. Correlation Matrix and cause-effect relationship.srt	11.7 KB
18. Correlation Matrix in R.mp4	99.6 MB
18. Correlation Matrix in R.srt	7.4 KB
19. Quiz.html	0.2 KB
2. Data Exploration.mp4	21.1 MB
2. Data Exploration.srt	3.9 KB
3. The Data and the Data Dictionary.mp4	82.1 MB
3. The Data and the Data Dictionary.srt	8.7 KB
4. Importing the dataset into R.mp4	16.7 MB
4. Importing the dataset into R.srt	2.9 KB
5. Univariate Analysis and EDD.mp4	28.5 MB
5. Univariate Analysis and EDD.srt	3.8 KB
6. EDD in R.mp4	117.4 MB
6. EDD in R.srt	14.1 KB
7. Outlier Treatment.mp4	28.6 MB
7. Outlier Treatment.srt	5.0 KB
8. Outlier Treatment in R.mp4	39.7 MB
8. Outlier Treatment in R.srt	5.0 KB
9. Missing Value imputation.mp4	24.3 MB
9. Missing Value imputation.srt	4.3 KB
/6. Linear Regression Model/
1. The problem statement.mp4	11.2 MB
1. The problem statement.srt	1.9 KB
10. Quiz.html	0.2 KB
11. Test-Train split.mp4	51.1 MB
11. Test-Train split.srt	12.9 KB
12. Bias Variance trade-off.mp4	30.8 MB
12. Bias Variance trade-off.srt	8.4 KB
13. More about test-train split.html	0.6 KB
14. Test-Train Split in R.mp4	95.3 MB
14. Test-Train Split in R.srt	9.8 KB
15. Assignment 1 Regression Analysis.html	0.2 KB
2. Basic equations and Ordinary Least Squared (OLS) method.mp4	52.3 MB
2. Basic equations and Ordinary Least Squared (OLS) method.srt	13.0 KB
3. Assessing Accuracy of predicted coefficients.mp4	108.9 MB
3. Assessing Accuracy of predicted coefficients.srt	20.4 KB
4. Assessing Model Accuracy - RSE and R squared.mp4	51.9 MB
4. Assessing Model Accuracy - RSE and R squared.srt	10.0 KB
5. Simple Linear Regression in R.mp4	52.9 MB
5. Simple Linear Regression in R.srt	9.8 KB
6. Multiple Linear Regression.mp4	40.6 MB
6. Multiple Linear Regression.srt	7.6 KB
7. The F - statistic.mp4	66.9 MB
7. The F - statistic.srt	11.7 KB
8. Interpreting result for categorical Variable.mp4	28.3 MB
8. Interpreting result for categorical Variable.srt	7.1 KB
9. Multiple Linear Regression in R.mp4	76.4 MB
9. Multiple Linear Regression in R.srt	9.8 KB
/7. Regression models other than OLS/
1. Linear models other than OLS.mp4	19.9 MB
1. Linear models other than OLS.srt	5.4 KB
2. Subset Selection techniques.mp4	90.9 MB
2. Subset Selection techniques.srt	15.6 KB
3. Subset selection in R.mp4	80.3 MB
3. Subset selection in R.srt	8.6 KB
4. Shrinkage methods - Ridge Regression and The Lasso.mp4	40.3 MB
4. Shrinkage methods - Ridge Regression and The Lasso.srt	9.6 KB
5. Ridge regression and Lasso in R.mp4	130.0 MB
5. Ridge regression and Lasso in R.srt	13.3 KB
/8. Introduction to the classification Models/
1. Three classification models and Data set.mp4	54.9 MB
1. Three classification models and Data set.srt	6.8 KB
1.1 Classification preprocessed data R.csv	42.0 KB
2. Importing the data into R.mp4	9.2 MB
2. Importing the data into R.srt	1.4 KB
2.1 Classification preprocessed data R.csv	52.2 KB
3. The problem statements.mp4	17.9 MB
3. The problem statements.srt	1.8 KB
4. Why can't we use Linear Regression.mp4	21.2 MB
4. Why can't we use Linear Regression.srt	5.8 KB
/9. Logistic Regression/
1. Logistic Regression.mp4	40.7 MB
1. Logistic Regression.srt	9.1 KB
2. Training a Simple Logistic model in R.mp4	32.5 MB
2. Training a Simple Logistic model in R.srt	4.4 KB
3. Results of Simple Logistic Regression.mp4	32.4 MB
3. Results of Simple Logistic Regression.srt	6.2 KB
4. Logistic with multiple predictors.mp4	10.4 MB
4. Logistic with multiple predictors.srt	3.2 KB
5. Training multiple predictor Logistic model in R.mp4	19.2 MB
5. Training multiple predictor Logistic model in R.srt	2.1 KB
6. Confusion Matrix.mp4	27.8 MB
6. Confusion Matrix.srt	5.3 KB
7. Evaluating Model performance.mp4	44.6 MB
7. Evaluating Model performance.srt	9.9 KB
8. Predicting probabilities, assigning classes and making Confusion Matrix in R.mp4	69.3 MB
8. Predicting probabilities, assigning classes and making Confusion Matrix in R.srt	7.8 KB
9. Quiz.html	0.2 KB
Total files 237