[UdemyCourseDownloader] Machine Learning Practical 6 Real-World Applications

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[UdemyCourseDownloader] Machine Learning Practical 6 Real-World Applications

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收录时间：2023-12-17
最近下载：2024-12-27

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文件列表

  • 2. Breast Cancer Classification/8. Improving the Model.mp4 199.0 MB
  • 2. Breast Cancer Classification/5. Data Visualisation.mp4 147.3 MB
  • 3. Fashion Class Classification/3. Data Visualisation.mp4 136.5 MB
  • 3. Fashion Class Classification/7. Model Training Part IV.mp4 134.4 MB
  • 3. Fashion Class Classification/6. Model Training Part III.mp4 132.1 MB
  • 3. Fashion Class Classification/4. Model Training Part I.mp4 108.8 MB
  • 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/2. Data.mp4 106.9 MB
  • 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/12. Grid Search Part 2.mp4 102.8 MB
  • 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/10. Model Building Part 2.mp4 98.0 MB
  • 3. Fashion Class Classification/1. Business Challenge.mp4 96.7 MB
  • 2. Breast Cancer Classification/7. Model Evaluation.mp4 95.8 MB
  • 5. Minimizing Churn Rate Through Analysis of Financial Habits/9. Feature Scaling & Balancing.mp4 83.6 MB
  • 2. Breast Cancer Classification/4. Challenge in Machine Learning Vocabulary.mp4 82.9 MB
  • 3. Fashion Class Classification/5. Model Training Part II.mp4 82.1 MB
  • 5. Minimizing Churn Rate Through Analysis of Financial Habits/2. Data.mp4 81.2 MB
  • 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/1. Introduction.mp4 78.8 MB
  • 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/11. Grid Search Part 1.mp4 78.0 MB
  • 3. Fashion Class Classification/8. Model Evaluation.mp4 77.2 MB
  • 4. Directing Customers to Subscription Through App Behavior Analysis/10. Model Building.mp4 75.8 MB
  • 4. Directing Customers to Subscription Through App Behavior Analysis/8. Feature Engineering - Screens.mp4 74.5 MB
  • 2. Breast Cancer Classification/6. Model Training.mp4 74.4 MB
  • 5. Minimizing Churn Rate Through Analysis of Financial Habits/5. Pie Chart Distributions.mp4 74.0 MB
  • 3. Fashion Class Classification/2. Challenge in Machine Learning Vocabulary.mp4 72.6 MB
  • 5. Minimizing Churn Rate Through Analysis of Financial Habits/12. Feature Selection.mp4 67.4 MB
  • 2. Breast Cancer Classification/2. Business Challenge.mp4 63.9 MB
  • 4. Directing Customers to Subscription Through App Behavior Analysis/4. Features Histograms.mp4 63.8 MB
  • 4. Directing Customers to Subscription Through App Behavior Analysis/9. Data Pre-Processing.mp4 63.6 MB
  • 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/8. Data Preprocessing.mp4 63.1 MB
  • 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/9. Model Building Part 1.mp4 61.6 MB
  • 2. Breast Cancer Classification/3. Updates on Udemy Reviews.mp4 58.8 MB
  • 5. Minimizing Churn Rate Through Analysis of Financial Habits/7. Correlation Matrix.mp4 58.8 MB
  • 3. Fashion Class Classification/10. Conclusion.mp4 58.0 MB
  • 4. Directing Customers to Subscription Through App Behavior Analysis/7. Feature Engineering - Response.mp4 57.6 MB
  • 4. Directing Customers to Subscription Through App Behavior Analysis/3. Data.mp4 56.2 MB
  • 5. Minimizing Churn Rate Through Analysis of Financial Habits/10. Model Building.mp4 53.8 MB
  • 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/4. Histograms.mp4 53.3 MB
  • 5. Minimizing Churn Rate Through Analysis of Financial Habits/4. Features Histograms.mp4 52.7 MB
  • 5. Minimizing Churn Rate Through Analysis of Financial Habits/6. Correlation Plot.mp4 51.1 MB
  • 7. Credit Card Fraud Detection/7. Deep Learning Part 2.mp4 50.5 MB
  • 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/3. Data Housekeeping.mp4 46.2 MB
  • 5. Minimizing Churn Rate Through Analysis of Financial Habits/8. One-Hot Encoding.mp4 45.0 MB
  • 7. Credit Card Fraud Detection/11. Confusion Matrix.mp4 42.0 MB
  • 7. Credit Card Fraud Detection/5. Data Preprocessing.mp4 40.9 MB
  • 5. Minimizing Churn Rate Through Analysis of Financial Habits/13. Model Conclusion.mp4 40.3 MB
  • 7. Credit Card Fraud Detection/8. Splitting the Data.mp4 40.1 MB
  • 7. Credit Card Fraud Detection/16. Undersampling.mp4 38.7 MB
  • 1. Introduction/1. Welcome to the course!.mp4 38.5 MB
  • 7. Credit Card Fraud Detection/17. Smote.mp4 37.4 MB
  • 7. Credit Card Fraud Detection/12. Machine Learning Classifiers.mp4 35.8 MB
  • 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/6. Correlation Matrix.mp4 35.0 MB
  • 5. Minimizing Churn Rate Through Analysis of Financial Habits/3. Data Cleaning.mp4 34.1 MB
  • 4. Directing Customers to Subscription Through App Behavior Analysis/6. Correlation Matrix.mp4 33.8 MB
  • 5. Minimizing Churn Rate Through Analysis of Financial Habits/11. K-Fold Cross Validation.mp4 33.2 MB
  • 3. Fashion Class Classification/9. Improving the Model.mp4 33.2 MB
  • 7. Credit Card Fraud Detection/13. Random Forest.mp4 32.5 MB
  • 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/14. Final Remarks.mp4 32.1 MB
  • 7. Credit Card Fraud Detection/1. Case Study.mp4 31.5 MB
  • 4. Directing Customers to Subscription Through App Behavior Analysis/11. Model Conclusion.mp4 31.4 MB
  • 5. Minimizing Churn Rate Through Analysis of Financial Habits/14. Final Remarks.mp4 25.5 MB
  • 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/5. Correlation Plot.mp4 25.5 MB
  • 2. Breast Cancer Classification/9. Conclusion.mp4 25.2 MB
  • 7. Credit Card Fraud Detection/3. Set Up.mp4 25.1 MB
  • 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/7. Feature Engineering.mp4 24.6 MB
  • 7. Credit Card Fraud Detection/6. Deep Learning Part 1.mp4 24.3 MB
  • 7. Credit Card Fraud Detection/2. Machine Learning Vocabulary.mp4 24.1 MB
  • 4. Directing Customers to Subscription Through App Behavior Analysis/5. Correlation Plot.mp4 23.1 MB
  • 7. Credit Card Fraud Detection/9. Training.mp4 22.2 MB
  • 7. Credit Card Fraud Detection/4. Data Visualization.mp4 21.4 MB
  • 5. Minimizing Churn Rate Through Analysis of Financial Habits/1. Introduction.mp4 20.6 MB
  • 7. Credit Card Fraud Detection/18. Final remarks.mp4 20.0 MB
  • 4. Directing Customers to Subscription Through App Behavior Analysis/12. Final Remarks.mp4 20.0 MB
  • 7. Credit Card Fraud Detection/14. Decision Trees.mp4 19.7 MB
  • 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/13. Model Conclusion.mp4 19.2 MB
  • 4. Directing Customers to Subscription Through App Behavior Analysis/2. Introduction.mp4 19.2 MB
  • 7. Credit Card Fraud Detection/10. Metrics.mp4 15.8 MB
  • 2. Breast Cancer Classification/1. Introduction.mp4 15.7 MB
  • 4. Directing Customers to Subscription Through App Behavior Analysis/1. Fintech Case Studies Introduction.mp4 15.3 MB
  • 7. Credit Card Fraud Detection/15. Sampling.mp4 8.1 MB
  • 2. Breast Cancer Classification/8. Improving the Model.vtt 29.8 kB
  • 2. Breast Cancer Classification/5. Data Visualisation.vtt 23.5 kB
  • 3. Fashion Class Classification/3. Data Visualisation.vtt 20.4 kB
  • 3. Fashion Class Classification/7. Model Training Part IV.vtt 19.9 kB
  • 3. Fashion Class Classification/6. Model Training Part III.vtt 14.7 kB
  • 2. Breast Cancer Classification/7. Model Evaluation.vtt 14.7 kB
  • 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/11. Grid Search Part 1.vtt 14.3 kB
  • 4. Directing Customers to Subscription Through App Behavior Analysis/10. Model Building.vtt 13.3 kB
  • 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/4. Histograms.vtt 12.9 kB
  • 3. Fashion Class Classification/8. Model Evaluation.vtt 12.5 kB
  • 3. Fashion Class Classification/4. Model Training Part I.vtt 12.1 kB
  • 5. Minimizing Churn Rate Through Analysis of Financial Habits/7. Correlation Matrix.vtt 12.0 kB
  • 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/10. Model Building Part 2.vtt 11.9 kB
  • 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/12. Grid Search Part 2.vtt 11.5 kB
  • 2. Breast Cancer Classification/6. Model Training.vtt 11.3 kB
  • 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/8. Data Preprocessing.vtt 11.2 kB
  • 5. Minimizing Churn Rate Through Analysis of Financial Habits/9. Feature Scaling & Balancing.vtt 11.2 kB
  • 5. Minimizing Churn Rate Through Analysis of Financial Habits/4. Features Histograms.vtt 11.0 kB
  • 5. Minimizing Churn Rate Through Analysis of Financial Habits/5. Pie Chart Distributions.vtt 10.9 kB
  • 4. Directing Customers to Subscription Through App Behavior Analysis/8. Feature Engineering - Screens.vtt 10.6 kB
  • 3. Fashion Class Classification/5. Model Training Part II.vtt 10.3 kB
  • 2. Breast Cancer Classification/4. Challenge in Machine Learning Vocabulary.vtt 10.2 kB
  • 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/2. Data.vtt 10.1 kB
  • 4. Directing Customers to Subscription Through App Behavior Analysis/9. Data Pre-Processing.vtt 10.0 kB
  • 5. Minimizing Churn Rate Through Analysis of Financial Habits/2. Data.vtt 10.0 kB
  • 4. Directing Customers to Subscription Through App Behavior Analysis/4. Features Histograms.vtt 10.0 kB
  • 4. Directing Customers to Subscription Through App Behavior Analysis/7. Feature Engineering - Response.vtt 10.0 kB
  • 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/1. Introduction.vtt 9.8 kB
  • 5. Minimizing Churn Rate Through Analysis of Financial Habits/10. Model Building.vtt 9.7 kB
  • 5. Minimizing Churn Rate Through Analysis of Financial Habits/6. Correlation Plot.vtt 9.6 kB
  • 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/6. Correlation Matrix.vtt 9.3 kB
  • 5. Minimizing Churn Rate Through Analysis of Financial Habits/12. Feature Selection.vtt 8.9 kB
  • 3. Fashion Class Classification/2. Challenge in Machine Learning Vocabulary.vtt 8.7 kB
  • 4. Directing Customers to Subscription Through App Behavior Analysis/6. Correlation Matrix.vtt 8.5 kB
  • 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/9. Model Building Part 1.vtt 8.2 kB
  • 7. Credit Card Fraud Detection/12. Machine Learning Classifiers.vtt 7.9 kB
  • 7. Credit Card Fraud Detection/7. Deep Learning Part 2.vtt 7.6 kB
  • 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/3. Data Housekeeping.vtt 7.0 kB
  • 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/5. Correlation Plot.vtt 6.8 kB
  • 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/7. Feature Engineering.vtt 6.7 kB
  • 5. Minimizing Churn Rate Through Analysis of Financial Habits/8. One-Hot Encoding.vtt 6.7 kB
  • 3. Fashion Class Classification/1. Business Challenge.vtt 6.0 kB
  • 5. Minimizing Churn Rate Through Analysis of Financial Habits/3. Data Cleaning.vtt 5.8 kB
  • 4. Directing Customers to Subscription Through App Behavior Analysis/5. Correlation Plot.vtt 5.6 kB
  • 5. Minimizing Churn Rate Through Analysis of Financial Habits/13. Model Conclusion.vtt 5.4 kB
  • 5. Minimizing Churn Rate Through Analysis of Financial Habits/11. K-Fold Cross Validation.vtt 5.4 kB
  • 3. Fashion Class Classification/10. Conclusion.vtt 4.7 kB
  • 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/14. Final Remarks.vtt 4.2 kB
  • 7. Credit Card Fraud Detection/8. Splitting the Data.vtt 4.2 kB
  • 4. Directing Customers to Subscription Through App Behavior Analysis/11. Model Conclusion.vtt 4.2 kB
  • 4. Directing Customers to Subscription Through App Behavior Analysis/3. Data.vtt 4.2 kB
  • 7. Credit Card Fraud Detection/11. Confusion Matrix.vtt 4.1 kB
  • 7. Credit Card Fraud Detection/10. Metrics.vtt 4.0 kB
  • 2. Breast Cancer Classification/2. Business Challenge.vtt 3.8 kB
  • 2. Breast Cancer Classification/9. Conclusion.vtt 3.7 kB
  • 2. Breast Cancer Classification/3. Updates on Udemy Reviews.vtt 3.6 kB
  • 7. Credit Card Fraud Detection/1. Case Study.vtt 3.6 kB
  • 3. Fashion Class Classification/9. Improving the Model.vtt 3.6 kB
  • 7. Credit Card Fraud Detection/6. Deep Learning Part 1.vtt 3.5 kB
  • 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/13. Model Conclusion.vtt 3.5 kB
  • 7. Credit Card Fraud Detection/2. Machine Learning Vocabulary.vtt 3.4 kB
  • 7. Credit Card Fraud Detection/3. Set Up.vtt 3.3 kB
  • 5. Minimizing Churn Rate Through Analysis of Financial Habits/14. Final Remarks.vtt 3.2 kB
  • 7. Credit Card Fraud Detection/13. Random Forest.vtt 3.2 kB
  • 7. Credit Card Fraud Detection/18. Final remarks.vtt 3.1 kB
  • 7. Credit Card Fraud Detection/5. Data Preprocessing.vtt 3.0 kB
  • 7. Credit Card Fraud Detection/14. Decision Trees.vtt 2.6 kB
  • 7. Credit Card Fraud Detection/16. Undersampling.vtt 2.6 kB
  • 5. Minimizing Churn Rate Through Analysis of Financial Habits/1. Introduction.vtt 2.6 kB
  • 7. Credit Card Fraud Detection/17. Smote.vtt 2.5 kB
  • 4. Directing Customers to Subscription Through App Behavior Analysis/12. Final Remarks.vtt 2.5 kB
  • 7. Credit Card Fraud Detection/4. Data Visualization.vtt 2.5 kB
  • 1. Introduction/1. Welcome to the course!.vtt 2.4 kB
  • 4. Directing Customers to Subscription Through App Behavior Analysis/2. Introduction.vtt 2.4 kB
  • 7. Credit Card Fraud Detection/15. Sampling.vtt 2.1 kB
  • 4. Directing Customers to Subscription Through App Behavior Analysis/1. Fintech Case Studies Introduction.vtt 2.1 kB
  • 7. Credit Card Fraud Detection/9. Training.vtt 1.9 kB
  • [FTU Forum].url 1.4 kB
  • 2. Breast Cancer Classification/1. Introduction.vtt 1.0 kB
  • [FreeCoursesOnline.Me].url 133 Bytes
  • [FreeTutorials.Eu].url 129 Bytes
  • 1. Introduction/2. Where to get the materials.html 128 Bytes

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