Udemy Machine Learning Project Heart Attack Prediction Analysis
Seeders : 6 Leechers : 14
| Torrent Hash : | 0FA7D08FE1762304D7DB2AABC23167B80FB51209 |
| Torrent Added : | at Oct. 23, 2023, 3:01 p.m. in Other |
| Torrent Size : | 2.1 GB |
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Udemy Machine Learning Project Heart Attack Prediction Analysis
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Udemy Machine Learning Project Heart Attack Prediction Analysis
Get Bonus Downloads Here.url -
1. First Step to the Hearth Attack Prediction Project.mp4 -
1. First Step to the Hearth Attack Prediction Project.srt -
2. FAQ about Machine Learning, Data Science.html -
3. Notebook Design to be Used in the Project.mp4 -
3. Notebook Design to be Used in the Project.srt -
4. Project Link File - Hearth Attack Prediction Project, Machine Learning.html -
5. Examining the Project Topic.mp4 -
5. Examining the Project Topic.srt -
6. Recognizing Variables In Dataset.mp4 -
6. Recognizing Variables In Dataset.srt -
1. Required Python Libraries.mp4 -
1. Required Python Libraries.srt -
2. Loading the Statistics Dataset in Data Science.mp4 -
2. Loading the Statistics Dataset in Data Science.srt -
3. Initial analysis on the dataset.mp4 -
3. Initial analysis on the dataset.srt -
1. Examining Missing Values.mp4 -
1. Examining Missing Values.srt -
2. Examining Unique Values.mp4 -
2. Examining Unique Values.srt -
3. Separating variables (Numeric or Categorical).mp4 -
3. Separating variables (Numeric or Categorical).srt -
4. Examining Statistics of Variables.mp4 -
4. Examining Statistics of Variables.srt -
1. Numeric Variables (Analysis with Distplot) Lesson 1.mp4 -
1. Numeric Variables (Analysis with Distplot) Lesson 1.srt -
2. Numeric Variables (Analysis with Distplot) Lesson 2.mp4 -
2. Numeric Variables (Analysis with Distplot) Lesson 2.srt -
3. Categoric Variables (Analysis with Pie Chart) Lesson 1.mp4 -
3. Categoric Variables (Analysis with Pie Chart) Lesson 1.srt -
4. Categoric Variables (Analysis with Pie Chart) Lesson 2.mp4 -
4. Categoric Variables (Analysis with Pie Chart) Lesson 2.srt -
5. Examining the Missing Data According to the Analysis Result.mp4 -
5. Examining the Missing Data According to the Analysis Result.srt -
1. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 1.mp4 -
1. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 1.srt -
10. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 2.mp4 -
10. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 2.srt -
11. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 1.mp4 -
11. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 1.srt -
12. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 2.mp4 -
12. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 2.srt -
13. Relationships between variables (Analysis with Heatmap) Lesson 1.mp4 -
13. Relationships between variables (Analysis with Heatmap) Lesson 1.srt -
14. Relationships between variables (Analysis with Heatmap) Lesson 2.mp4 -
14. Relationships between variables (Analysis with Heatmap) Lesson 2.srt -
2. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 2.mp4 -
2. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 2.srt -
3. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 1.mp4 -
3. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 1.srt -
4. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 2.mp4 -
4. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 2.srt -
5. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 1.mp4 -
5. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 1.srt -
6. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 2.mp4 -
6. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 2.srt -
7. Feature Scaling with the Robust Scaler Method.mp4 -
7. Feature Scaling with the Robust Scaler Method.srt -
8. Creating a New DataFrame with the Melt() Function.mp4 -
8. Creating a New DataFrame with the Melt() Function.srt -
9. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 1.mp4 -
9. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 1.srt -
1. Dropping Columns with Low Correlation.mp4 -
1. Dropping Columns with Low Correlation.srt -
10. Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms.mp4 -
10. Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms.srt -
11. Separating Data into Test and Training Set.mp4 -
11. Separating Data into Test and Training Set.srt -
2. Visualizing Outliers.mp4 -
2. Visualizing Outliers.srt -
3. Dealing with Outliers – Trtbps Variable Lesson 1.mp4 -
3. Dealing with Outliers – Trtbps Variable Lesson 1.srt -
4. Dealing with Outliers – Trtbps Variable Lesson 2.mp4 -
4. Dealing with Outliers – Trtbps Variable Lesson 2.srt -
5. Dealing with Outliers – Thalach Variable.mp4 -
5. Dealing with Outliers – Thalach Variable.srt -
6. Dealing with Outliers – Oldpeak Variable.mp4 -
6. Dealing with Outliers – Oldpeak Variable.srt -
7. Determining Distributions of Numeric Variables.mp4 -
7. Determining Distributions of Numeric Variables.srt -
8. Transformation Operations on Unsymmetrical Data.mp4 -
8. Transformation Operations on Unsymmetrical Data.srt -
9. Applying One Hot Encoding Method to Categorical Variables.mp4 -
9. Applying One Hot Encoding Method to Categorical Variables.srt -
1. Logistic Regression.mp4 -
1. Logistic Regression.srt -
2. Cross Validation.mp4 -
2. Cross Validation.srt -
3. Roc Curve and Area Under Curve (AUC).mp4 -
3. Roc Curve and Area Under Curve (AUC).srt -
4. Hyperparameter Optimization (with GridSearchCV).mp4 -
4. Hyperparameter Optimization (with GridSearchCV).srt -
5. Decision Tree Algorithm.mp4 -
5. Decision Tree Algorithm.srt -
6. Support Vector Machine Algorithm.mp4 -
6. Support Vector Machine Algorithm.srt -
7. Random Forest Algorithm.mp4 -
7. Random Forest Algorithm.srt -
8. Hyperparameter Optimization (with GridSearchCV).mp4 -
8. Hyperparameter Optimization (with GridSearchCV).srt -
1. Project Conclusion and Sharing.mp4 -
1. Project Conclusion and Sharing.srt -
1. Machine Learning with Real Hearth Attack Prediction Project.html -
Bonus Resources.txt -
Please login or create a FREE account to post comments
Get Bonus Downloads Here.url -
183 bytes
1. First Step to the Hearth Attack Prediction Project.mp4 -
108.6 MB
1. First Step to the Hearth Attack Prediction Project.srt -
21.4 KB
2. FAQ about Machine Learning, Data Science.html -
15.3 KB
3. Notebook Design to be Used in the Project.mp4 -
97.7 MB
3. Notebook Design to be Used in the Project.srt -
20.3 KB
4. Project Link File - Hearth Attack Prediction Project, Machine Learning.html -
108 bytes
5. Examining the Project Topic.mp4 -
71.7 MB
5. Examining the Project Topic.srt -
13.9 KB
6. Recognizing Variables In Dataset.mp4 -
115.3 MB
6. Recognizing Variables In Dataset.srt -
23.8 KB
1. Required Python Libraries.mp4 -
58.8 MB
1. Required Python Libraries.srt -
12.8 KB
2. Loading the Statistics Dataset in Data Science.mp4 -
9.3 MB
2. Loading the Statistics Dataset in Data Science.srt -
2.7 KB
3. Initial analysis on the dataset.mp4 -
58.7 MB
3. Initial analysis on the dataset.srt -
18.2 KB
1. Examining Missing Values.mp4 -
42.4 MB
1. Examining Missing Values.srt -
13.2 KB
2. Examining Unique Values.mp4 -
41.0 MB
2. Examining Unique Values.srt -
12.9 KB
3. Separating variables (Numeric or Categorical).mp4 -
14.7 MB
3. Separating variables (Numeric or Categorical).srt -
4.6 KB
4. Examining Statistics of Variables.mp4 -
84.3 MB
4. Examining Statistics of Variables.srt -
24.6 KB
1. Numeric Variables (Analysis with Distplot) Lesson 1.mp4 -
74.6 MB
1. Numeric Variables (Analysis with Distplot) Lesson 1.srt -
20.2 KB
2. Numeric Variables (Analysis with Distplot) Lesson 2.mp4 -
18.3 MB
2. Numeric Variables (Analysis with Distplot) Lesson 2.srt -
5.3 KB
3. Categoric Variables (Analysis with Pie Chart) Lesson 1.mp4 -
69.0 MB
3. Categoric Variables (Analysis with Pie Chart) Lesson 1.srt -
19.7 KB
4. Categoric Variables (Analysis with Pie Chart) Lesson 2.mp4 -
78.0 MB
4. Categoric Variables (Analysis with Pie Chart) Lesson 2.srt -
20.9 KB
5. Examining the Missing Data According to the Analysis Result.mp4 -
50.0 MB
5. Examining the Missing Data According to the Analysis Result.srt -
13.9 KB
1. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 1.mp4 -
45.3 MB
1. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 1.srt -
11.3 KB
10. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 2.mp4 -
64.0 MB
10. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 2.srt -
15.4 KB
11. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 1.mp4 -
36.0 MB
11. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 1.srt -
10.1 KB
12. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 2.mp4 -
32.8 MB
12. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 2.srt -
10.3 KB
13. Relationships between variables (Analysis with Heatmap) Lesson 1.mp4 -
33.7 MB
13. Relationships between variables (Analysis with Heatmap) Lesson 1.srt -
8.7 KB
14. Relationships between variables (Analysis with Heatmap) Lesson 2.mp4 -
82.5 MB
14. Relationships between variables (Analysis with Heatmap) Lesson 2.srt -
16.0 KB
2. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 2.mp4 -
32.8 MB
2. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 2.srt -
9.7 KB
3. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 1.mp4 -
22.3 MB
3. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 1.srt -
5.0 KB
4. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 2.mp4 -
52.3 MB
4. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 2.srt -
16.7 KB
5. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 1.mp4 -
26.6 MB
5. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 1.srt -
7.1 KB
6. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 2.mp4 -
43.9 MB
6. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 2.srt -
8.9 KB
7. Feature Scaling with the Robust Scaler Method.mp4 -
32.6 MB
7. Feature Scaling with the Robust Scaler Method.srt -
11.7 KB
8. Creating a New DataFrame with the Melt() Function.mp4 -
48.8 MB
8. Creating a New DataFrame with the Melt() Function.srt -
15.1 KB
9. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 1.mp4 -
39.3 MB
9. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 1.srt -
8.3 KB
1. Dropping Columns with Low Correlation.mp4 -
24.8 MB
1. Dropping Columns with Low Correlation.srt -
5.2 KB
10. Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms.mp4 -
10.6 MB
10. Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms.srt -
3.1 KB
11. Separating Data into Test and Training Set.mp4 -
27.8 MB
11. Separating Data into Test and Training Set.srt -
9.4 KB
2. Visualizing Outliers.mp4 -
32.7 MB
2. Visualizing Outliers.srt -
11.9 KB
3. Dealing with Outliers – Trtbps Variable Lesson 1.mp4 -
40.0 MB
3. Dealing with Outliers – Trtbps Variable Lesson 1.srt -
13.7 KB
4. Dealing with Outliers – Trtbps Variable Lesson 2.mp4 -
40.8 MB
4. Dealing with Outliers – Trtbps Variable Lesson 2.srt -
15.2 KB
5. Dealing with Outliers – Thalach Variable.mp4 -
33.7 MB
5. Dealing with Outliers – Thalach Variable.srt -
11.2 KB
6. Dealing with Outliers – Oldpeak Variable.mp4 -
33.3 MB
6. Dealing with Outliers – Oldpeak Variable.srt -
11.0 KB
7. Determining Distributions of Numeric Variables.mp4 -
23.3 MB
7. Determining Distributions of Numeric Variables.srt -
6.5 KB
8. Transformation Operations on Unsymmetrical Data.mp4 -
22.2 MB
8. Transformation Operations on Unsymmetrical Data.srt -
6.3 KB
9. Applying One Hot Encoding Method to Categorical Variables.mp4 -
22.4 MB
9. Applying One Hot Encoding Method to Categorical Variables.srt -
7.6 KB
1. Logistic Regression.mp4 -
27.3 MB
1. Logistic Regression.srt -
9.1 KB
2. Cross Validation.mp4 -
28.2 MB
2. Cross Validation.srt -
7.6 KB
3. Roc Curve and Area Under Curve (AUC).mp4 -
38.6 MB
3. Roc Curve and Area Under Curve (AUC).srt -
10.2 KB
4. Hyperparameter Optimization (with GridSearchCV).mp4 -
54.7 MB
4. Hyperparameter Optimization (with GridSearchCV).srt -
17.4 KB
5. Decision Tree Algorithm.mp4 -
24.0 MB
5. Decision Tree Algorithm.srt -
7.4 KB
6. Support Vector Machine Algorithm.mp4 -
22.7 MB
6. Support Vector Machine Algorithm.srt -
6.6 KB
7. Random Forest Algorithm.mp4 -
27.7 MB
7. Random Forest Algorithm.srt -
8.4 KB
8. Hyperparameter Optimization (with GridSearchCV).mp4 -
48.6 MB
8. Hyperparameter Optimization (with GridSearchCV).srt -
14.4 KB
1. Project Conclusion and Sharing.mp4 -
27.0 MB
1. Project Conclusion and Sharing.srt -
4.9 KB
1. Machine Learning with Real Hearth Attack Prediction Project.html -
266 bytes
Bonus Resources.txt -
386 bytes
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