Machine Learning Made Easy Beginner to Advanced using | FFC8ABD4AA28467122201DA7FABCCDF1D2A46904

Machine Learning Made Easy Beginner to Advanced using

Name	Machine Learning Made Easy - Beginner to Advanced using R	DOWNLOAD Copy Link
Total Size	2.1 GB
Total Files	309
Hash	FFC8ABD4AA28467122201DA7FABCCDF1D2A46904

/.../2. Data Handling in R/
1.3 Section 2. Data Handling Dataset.zip.zip	42.6 MB
1. Introduction to Data Handling.mp4	2.8 MB
1. Introduction to Data Handling.srt	2.2 KB
1.1 Section 2.R Data Handling.R.zip.zip	2.7 KB
1.2 2. R DataHandling_Class_v1.pdf.pdf	534.1 KB
2. Importing the Datasets.mp4	12.9 MB
2. Importing the Datasets.srt	9.5 KB
3. Checklist.mp4	12.7 MB
3. Checklist.srt	7.8 KB
4. Subsetting the Data.mp4	10.6 MB
4. Subsetting the Data.srt	6.6 KB
5. Subsetting Variable Condition.mp4	15.7 MB
5. Subsetting Variable Condition.srt	7.7 KB
6. Calculated Fields_ifelse.mp4	16.6 MB
6. Calculated Fields_ifelse.srt	10.6 KB
7. Sorting and Duplicates.mp4	22.7 MB
7. Sorting and Duplicates.srt	13.5 KB
8. Joining and Merging.mp4	12.6 MB
8. Joining and Merging.srt	7.7 KB
9. Exporting the Data.mp4	9.0 MB
9. Exporting the Data.srt	4.6 KB
10. Data handling quiz.html	0.2 KB
/
Visit Coursedrive.org.url	0.1 KB
ReadMe.txt	0.2 KB
/Machine Learning Made Easy - Beginner to Advanced using R/
ReadMe.txt	0.2 KB
Visit Coursedrive.org.url	0.1 KB
/1. Introduction to R/
1. Getting Started.mp4	10.7 MB
1. Getting Started.srt	7.1 KB
1.1 1.R Introuction Class_v2.pdf.pdf	669.4 KB
1.2 Section 1. R Introduction.R.zip.zip	1.6 KB
2. R Environment.mp4	10.1 MB
2. R Environment.srt	7.0 KB
3. R Packages.mp4	11.6 MB
3. R Packages.srt	5.6 KB
4. R Data types Vectors.mp4	33.3 MB
4. R Data types Vectors.srt	14.6 KB
5. R Data Frames.mp4	28.3 MB
5. R Data Frames.srt	17.5 KB
6. List.mp4	22.1 MB
6. List.srt	14.1 KB
7. Factor and Matrix.mp4	8.1 MB
7. Factor and Matrix.srt	5.0 KB
8. R History and Scripts.mp4	23.2 MB
8. R History and Scripts.srt	13.0 KB
9. R Functions.mp4	10.2 MB
9. R Functions.srt	7.3 KB
10. Errors.mp4	16.0 MB
10. Errors.srt	12.1 KB
11. Introduction to R quiz.html	0.2 KB
/.../3. Basic Statistics and Graph/
1. Introduction and Sampling.mp4	6.7 MB
1. Introduction and Sampling.srt	5.6 KB
1.1 3. R Basic Statistics Graphs and Reporting_Class_v2 .pdf.pdf	643.0 KB
1.2 Section 3. R Basic Statistics Dataset.zip.zip	39.0 MB
1.3 Section 3. R Basic Statistics Graphs and Reporting .R.zip.zip	1.3 KB
2. Descriptive Statistics.mp4	19.1 MB
2. Descriptive Statistics.srt	13.3 KB
3. Percentiles and Quartiles.mp4	9.0 MB
3. Percentiles and Quartiles.srt	6.5 KB
4. Box Plots.mp4	10.7 MB
4. Box Plots.srt	6.6 KB
5. Creating Graphs and Conclusions.mp4	17.4 MB
5. Creating Graphs and Conclusions.srt	10.2 KB
6. Basic Statistics and graph quiz.html	0.2 KB
/.../4. Data Cleaning and Treatment/
1. Introduction to Data Cleaning and Model Building Cycle.mp4	3.9 MB
1. Introduction to Data Cleaning and Model Building Cycle.srt	3.2 KB
1.1 Section 4. Datasets.zip.zip	2.8 MB
1.2 Section 4. Data Cleaning Preparing data for Analysis_v2.R.zip.zip	1.9 KB
1.3 4. R.Data Cleaning Preparing data for Analysis_Class_v1.pdf.pdf	963.2 KB
2. Model Building Cycle.mp4	13.5 MB
2. Model Building Cycle.srt	10.9 KB
3. Data Cleaning Case Study.mp4	14.5 MB
3. Data Cleaning Case Study.srt	9.9 KB
4. CS lab step one basic content of dataset.mp4	25.5 MB
4. CS lab step one basic content of dataset.srt	14.0 KB
5. Variable Level Exploration Catagorical.mp4	8.2 MB
5. Variable Level Exploration Catagorical.srt	6.7 KB
6. Reading Data Dictionary.mp4	26.9 MB
6. Reading Data Dictionary.srt	14.1 KB
7. Step two Lab Categorical Variable Exploration.mp4	31.4 MB
7. Step two Lab Categorical Variable Exploration.srt	17.2 KB
8. Step three Lab Variable Level Exploration Continues.mp4	30.3 MB
8. Step three Lab Variable Level Exploration Continues.srt	14.3 KB
9. Data Cleaning and Treatment.mp4	14.2 MB
9. Data Cleaning and Treatment.srt	10.6 KB
10. Step four Treatment-Scenario 1.mp4	16.2 MB
10. Step four Treatment-Scenario 1.srt	9.5 KB
11. Step four Treatment-Scenario 2.mp4	22.0 MB
11. Step four Treatment-Scenario 2.srt	13.2 KB
12. Data Cleaning Scenario 3.mp4	12.2 MB
12. Data Cleaning Scenario 3.srt	7.8 KB
13. Some Other Variables.mp4	5.5 MB
13. Some Other Variables.srt	3.7 KB
14. Conclusions.mp4	4.1 MB
14. Conclusions.srt	2.9 KB
/5. Linear Regression/
1. Introduction and Correlation.mp4	28.6 MB
1. Introduction and Correlation.srt	5.6 KB
1.1 Section 5. Regression Datasets.zip.zip	18.0 KB
1.2 Section 5. Regression code.R.zip.zip	1.4 KB
1.3 5.Regression_in_R_Classv1.pdf.pdf	2.1 MB
2. LBA Correlation Calculation in R.mp4	8.5 MB
2. LBA Correlation Calculation in R.srt	6.4 KB
3. Beyond Pearson Correlation.mp4	5.7 MB
3. Beyond Pearson Correlation.srt	4.7 KB
4. From Correlation to Regression.mp4	16.1 MB
4. From Correlation to Regression.srt	13.7 KB
5. Regression Line Fitting in R.mp4	15.2 MB
5. Regression Line Fitting in R.srt	10.2 KB
6. R Squared.mp4	21.9 MB
6. R Squared.srt	16.2 KB
7. Multiple Regression.mp4	18.6 MB
7. Multiple Regression.srt	12.9 KB
8. Adjusted R Squared.mp4	8.2 MB
8. Adjusted R Squared.srt	6.7 KB
9. Issue with Multiple Regression.mp4	25.1 MB
9. Issue with Multiple Regression.srt	14.9 KB
10. Multicollinearity.mp4	23.9 MB
10. Multicollinearity.srt	17.6 KB
11. Regression Conclusion.mp4	4.3 MB
11. Regression Conclusion.srt	2.9 KB
12. Regression Quiz.html	0.2 KB
/6. Logistic Regression/
1. Need of Non-Linear Regression.mp4	25.3 MB
1. Need of Non-Linear Regression.srt	19.8 KB
1.1 Section 6. Logistic Regression_Code.R.zip.zip	1.4 KB
1.2 6.Logistic Regression_in_R_Classv1.pdf.pdf	991.2 KB
1.3 Section 6. Logistic Regression Datasets.zip.zip	504.1 KB
2. Logistic Function and Line.mp4	17.1 MB
2. Logistic Function and Line.srt	13.0 KB
3. Multiple Logistic Regression.mp4	14.1 MB
3. Multiple Logistic Regression.srt	10.5 KB
4. Goodness of Fit for a Logistic Regression.mp4	21.6 MB
4. Goodness of Fit for a Logistic Regression.srt	16.0 KB
5. Multicollinearity in Logistic Regression.mp4	12.8 MB
5. Multicollinearity in Logistic Regression.srt	9.8 KB
6. Individual Impact of Variables.mp4	10.2 MB
6. Individual Impact of Variables.srt	6.5 KB
7. Model Selection.mp4	24.8 MB
7. Model Selection.srt	16.0 KB
8. Logistic Regression Conclusion.mp4	2.5 MB
8. Logistic Regression Conclusion.srt	1.9 KB
9. Logistic Regression Quiz.html	0.2 KB
/7. Decision Tree/
1. Introduction to Decision Tree and Segmentation.mp4	11.6 MB
1. Introduction to Decision Tree and Segmentation.srt	9.0 KB
1.1 Section 7. Decision Tree Datasets.zip.zip	589.7 KB
1.2 Section 7. Decision Trees code.R.zip.zip	1.3 KB
1.3 7.Decision_Trees_in_R_Classv1.pdf.pdf	2.1 MB
2. The Decision Tree Philosophy & The Decision Tree Approach.mp4	26.6 MB
2. The Decision Tree Philosophy & The Decision Tree Approach.srt	19.1 KB
3. The Splitting Criterion & Entropy Calculation.mp4	29.1 MB
3. The Splitting Criterion & Entropy Calculation.srt	19.2 KB
4. Information Gain & Calculation.mp4	16.7 MB
4. Information Gain & Calculation.srt	11.6 KB
5. The Decision Tree Algorithm.mp4	19.6 MB
5. The Decision Tree Algorithm.srt	15.7 KB
6. Split for Variable & The Decision Tree Lab - Part 1.mp4	29.9 MB
6. Split for Variable & The Decision Tree Lab - Part 1.srt	19.0 KB
7. The Decision Tree Lab - Part 2 & Validation.mp4	28.7 MB
7. The Decision Tree Lab - Part 2 & Validation.srt	17.0 KB
8. The Decision Tree Lab - Part 3 & Overfitting.mp4	38.5 MB
8. The Decision Tree Lab - Part 3 & Overfitting.srt	20.6 KB
9. Pruning & Complexity Parameters.mp4	10.4 MB
9. Pruning & Complexity Parameters.srt	6.6 KB
10. Choosing Cp & Cross Validation Error.mp4	24.9 MB
10. Choosing Cp & Cross Validation Error.srt	13.6 KB
11. Two Types of Pruning.mp4	7.3 MB
11. Two Types of Pruning.srt	3.4 KB
12. Tree Building and Model Selection.mp4	38.9 MB
12. Tree Building and Model Selection.srt	18.4 KB
13. Conclusion.mp4	3.6 MB
13. Conclusion.srt	2.7 KB
14. Decision Trees Quiz.html	0.2 KB
/.../8. Model Selection and Cross Validation/
1. Introduction to Model Selection.mp4	3.6 MB
1. Introduction to Model Selection.srt	2.7 KB
1.1 Section 8. Model Selection Cross Validation_v3.R.zip.zip	1.8 KB
1.2 8. Model Selection and Cross Validation_in_R_Classv3.pdf.pdf	2.0 MB
1.3 Section 8. MSCV Datasets.zip.zip	504.1 KB
2. Sensitivity Specificity.mp4	20.1 MB
2. Sensitivity Specificity.srt	12.7 KB
3. Sensitivity Specificity Continued.mp4	19.3 MB
3. Sensitivity Specificity Continued.srt	13.2 KB
4. ROC AUC.mp4	23.5 MB
4. ROC AUC.srt	11.6 KB
5. The Best Model.mp4	10.1 MB
5. The Best Model.srt	5.1 KB
6. Errors.mp4	10.6 MB
6. Errors.srt	7.6 KB
7. Overfitting Underfitting.mp4	29.9 MB
7. Overfitting Underfitting.srt	15.2 KB
8. Bias_Variance Treadoff.mp4	17.6 MB
8. Bias_Variance Treadoff.srt	11.7 KB
9. Holdout Data Validation.mp4	12.9 MB
9. Holdout Data Validation.srt	5.8 KB
10. Ten fold CV.mp4	24.8 MB
10. Ten fold CV.srt	12.5 KB
11. Kfold CV.mp4	19.2 MB
11. Kfold CV.srt	11.4 KB
12. MSCV Conclusion.mp4	3.3 MB
12. MSCV Conclusion.srt	2.4 KB
13. Model selection cross validation Quiz.html	0.2 KB
/9. Neural Networks/
1. Introduction and Logistic Regression Recap.mp4	16.8 MB
1. Introduction and Logistic Regression Recap.srt	9.1 KB
1.1 Section 9. Neural Net Datasets.zip.zip	4.5 MB
1.2 9.Neural Network_in_R_Classv2.pdf.pdf	3.4 MB
1.3 Section 9.Neural Network_v7.R.zip.zip	3.2 KB
2. Decision Boundary.mp4	6.7 MB
2. Decision Boundary.srt	3.6 KB
3. Non Linear Decision Boundary NN.mp4	14.8 MB
3. Non Linear Decision Boundary NN.srt	8.0 KB
4. Non Linear Decision Boundary and Solution.mp4	29.8 MB
4. Non Linear Decision Boundary and Solution.srt	13.1 KB
5. Neural Net Intution.mp4	14.4 MB
5. Neural Net Intution.srt	9.6 KB
6. Neural Net Algorithm.mp4	12.9 MB
6. Neural Net Algorithm.srt	9.6 KB
7. Neural Net Algorithm Demo.mp4	12.3 MB
7. Neural Net Algorithm Demo.srt	7.9 KB
8. Building a Neural Network.mp4	25.8 MB
8. Building a Neural Network.srt	12.7 KB
9. Local Vs Global Min.mp4	10.6 MB
9. Local Vs Global Min.srt	6.3 KB
10. Digit Recognizer second attempt part1.mp4	12.3 MB
10. Digit Recognizer second attempt part1.srt	4.9 KB
11. Digit Recognizer second attempt part2.mp4	19.1 MB
11. Digit Recognizer second attempt part2.srt	8.9 KB
12. Lab Digit Reconizer.mp4	11.7 MB
12. Lab Digit Reconizer.srt	4.6 KB
13. Conclusion.mp4	11.7 MB
13. Conclusion.srt	6.2 KB
14. Neural Networks.html	0.2 KB
/.../10. Support Vector Machines/
1. Introduction to SVM.mp4	3.9 MB
1. Introduction to SVM.srt	2.2 KB
1.1 Section 10. SVM Datasets.zip.zip	4.9 MB
1.2 Section 10. SVM code.R.zip.zip	1.8 KB
1.3 10.SVM_in_R_Classv4.pdf.pdf	2.1 MB
2. The Classifier and Decision Boundary.mp4	11.9 MB
2. The Classifier and Decision Boundary.srt	6.4 KB
3. SVM- The Large Margin Classifier.mp4	3.1 MB
3. SVM- The Large Margin Classifier.srt	1.8 KB
4. The SVM Alogirithm and Results.mp4	7.4 MB
4. The SVM Alogirithm and Results.srt	4.7 KB
5. SVM on R.mp4	12.1 MB
5. SVM on R.srt	5.1 KB
6. Non Linear Boundary.mp4	6.8 MB
6. Non Linear Boundary.srt	4.1 KB
7. Kernal Trick.mp4	11.4 MB
7. Kernal Trick.srt	7.4 KB
8. Kernal Trick on R.mp4	18.5 MB
8. Kernal Trick on R.srt	7.3 KB
9. Soft Margin and Validation.mp4	6.7 MB
9. Soft Margin and Validation.srt	4.6 KB
10. SVM Advantage, Disadvantage and Applications.mp4	5.2 MB
10. SVM Advantage, Disadvantage and Applications.srt	3.5 KB
11. Lab Digit Reconizer.mp4	26.0 MB
11. Lab Digit Reconizer.srt	8.2 KB
12. SVM Conclusion.mp4	2.1 MB
12. SVM Conclusion.srt	1.1 KB
13. support vector machine.html	0.2 KB
/.../11. Ensamble Learning, Random Forest and Boosting/
1. Introduction to Bagging RF Boosting.mp4	1.5 MB
1. Introduction to Bagging RF Boosting.srt	1.0 KB
1.1 11.2.Basic Boosted Models.pdf.pdf	932.3 KB
1.2 11.1.Basic Ensemble Models & Random Forests_R _v2.pdf.pdf	932.4 KB
1.3 Section 11. Random Forest Datasets.zip.zip	18.5 MB
1.4 Section 11. Random Forest code.R.zip.zip	2.2 KB
2. Wisdom of Crowd.mp4	10.9 MB
2. Wisdom of Crowd.srt	7.7 KB
3. Ensemble Learning.mp4	11.5 MB
3. Ensemble Learning.srt	6.8 KB
4. Ensamble Models.mp4	12.1 MB
4. Ensamble Models.srt	7.2 KB
5. Bagging.mp4	15.0 MB
5. Bagging.srt	9.5 KB
6. Bagging Models.mp4	17.3 MB
6. Bagging Models.srt	7.6 KB
7. Random Forest.mp4	23.2 MB
7. Random Forest.srt	14.3 KB
8. Random Forest Lab.mp4	13.7 MB
8. Random Forest Lab.srt	6.3 KB
9. Boosting.mp4	13.8 MB
9. Boosting.srt	9.0 KB
10. Boosting Illustration.mp4	15.5 MB
10. Boosting Illustration.srt	10.7 KB
11. Boosting Lab.mp4	30.7 MB
11. Boosting Lab.srt	12.1 KB
12. Conclusion.mp4	7.4 MB
12. Conclusion.srt	4.8 KB
13. Random forest and boosting.html	0.2 KB
/.../12. Cluster Analysis/
1. Introduction to Clustering via Segmentation.mp4	17.3 MB
1. Introduction to Clustering via Segmentation.srt	12.7 KB
1.1 12.Cluster Aalysis in R Class V3.pdf.pdf	1.3 MB
1.2 Section 12. Cluster Analysis DataSets.zip.zip	35.1 KB
1.3 Section 12. Cluster Analysis Code_v3.R.zip.zip	1.5 KB
2. Types of Cluster.mp4	8.1 MB
2. Types of Cluster.srt	6.5 KB
3. Similiarities and Dissimilarity.mp4	11.6 MB
3. Similiarities and Dissimilarity.srt	9.1 KB
4. Calculating the Distance.mp4	8.5 MB
4. Calculating the Distance.srt	7.3 KB
5. Calculating Distance in R.mp4	10.7 MB
5. Calculating Distance in R.srt	7.1 KB
6. Clustering Algorithms- Kmeans.mp4	23.1 MB
6. Clustering Algorithms- Kmeans.srt	16.5 KB
7. Kmeans Clustering on R.mp4	27.4 MB
7. Kmeans Clustering on R.srt	14.4 KB
8. More on Kmeans.mp4	26.9 MB
8. More on Kmeans.srt	14.5 KB
9. Data Standardisation and Non-numeric Data.mp4	22.8 MB
9. Data Standardisation and Non-numeric Data.srt	17.1 KB
10. Clustering Conclusion.mp4	4.3 MB
10. Clustering Conclusion.srt	3.6 KB
11. Cluster Analysis.html	0.2 KB
Total files 309