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Udemy Machine Learning Project Heart Attack Prediction Analysis

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Udemy Machine Learning Project Heart Attack Prediction Analysis

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Files:

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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