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[FreeTutorials.Eu] Udemy - Machine Learning Practical 6 Real-World Applications

文件类型 收录时间 最后活跃 资源热度 文件大小 文件数量
视频 2019-10-26 00:07 2024-12-22 05:19 161 4.05 GB 78
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FreeTutorials Eu Udemy - Machine Learning Practical 6 Real-World Applications

文件列表
  1. 1. Introduction/1. Welcome to the course!.mp436.75MB
  2. 2. Breast Cancer Classification/1. Introduction.mp414.97MB
  3. 2. Breast Cancer Classification/2. Business Challenge.mp460.98MB
  4. 2. Breast Cancer Classification/3. Updates on Udemy Reviews.mp456.12MB
  5. 2. Breast Cancer Classification/4. Challenge in Machine Learning Vocabulary.mp479.06MB
  6. 2. Breast Cancer Classification/5. Data Visualisation.mp4140.5MB
  7. 2. Breast Cancer Classification/6. Model Training.mp470.94MB
  8. 2. Breast Cancer Classification/7. Model Evaluation.mp491.38MB
  9. 2. Breast Cancer Classification/8. Improving the Model.mp4189.77MB
  10. 2. Breast Cancer Classification/9. Conclusion.mp424.04MB
  11. 3. Fashion Class Classification/10. Conclusion.mp455.27MB
  12. 3. Fashion Class Classification/1. Business Challenge.mp492.22MB
  13. 3. Fashion Class Classification/2. Challenge in Machine Learning Vocabulary.mp469.24MB
  14. 3. Fashion Class Classification/3. Data Visualisation.mp4130.14MB
  15. 3. Fashion Class Classification/4. Model Training Part I.mp4103.76MB
  16. 3. Fashion Class Classification/5. Model Training Part II.mp478.3MB
  17. 3. Fashion Class Classification/6. Model Training Part III.mp4125.99MB
  18. 3. Fashion Class Classification/7. Model Training Part IV.mp4128.19MB
  19. 3. Fashion Class Classification/8. Model Evaluation.mp473.61MB
  20. 3. Fashion Class Classification/9. Improving the Model.mp431.62MB
  21. 4. Directing Customers to Subscription Through App Behavior Analysis/10. Model Building.mp472.33MB
  22. 4. Directing Customers to Subscription Through App Behavior Analysis/11. Model Conclusion.mp429.99MB
  23. 4. Directing Customers to Subscription Through App Behavior Analysis/12. Final Remarks.mp419.05MB
  24. 4. Directing Customers to Subscription Through App Behavior Analysis/1. Fintech Case Studies Introduction.mp414.63MB
  25. 4. Directing Customers to Subscription Through App Behavior Analysis/2. Introduction.mp418.28MB
  26. 4. Directing Customers to Subscription Through App Behavior Analysis/3. Data.mp453.63MB
  27. 4. Directing Customers to Subscription Through App Behavior Analysis/4. Features Histograms.mp460.84MB
  28. 4. Directing Customers to Subscription Through App Behavior Analysis/5. Correlation Plot.mp422.04MB
  29. 4. Directing Customers to Subscription Through App Behavior Analysis/6. Correlation Matrix.mp432.28MB
  30. 4. Directing Customers to Subscription Through App Behavior Analysis/7. Feature Engineering - Response.mp454.89MB
  31. 4. Directing Customers to Subscription Through App Behavior Analysis/8. Feature Engineering - Screens.mp471.04MB
  32. 4. Directing Customers to Subscription Through App Behavior Analysis/9. Data Pre-Processing.mp460.65MB
  33. 5. Minimizing Churn Rate Through Analysis of Financial Habits/10. Model Building.mp451.26MB
  34. 5. Minimizing Churn Rate Through Analysis of Financial Habits/11. K-Fold Cross Validation.mp431.69MB
  35. 5. Minimizing Churn Rate Through Analysis of Financial Habits/12. Feature Selection.mp464.3MB
  36. 5. Minimizing Churn Rate Through Analysis of Financial Habits/13. Model Conclusion.mp438.42MB
  37. 5. Minimizing Churn Rate Through Analysis of Financial Habits/14. Final Remarks.mp424.29MB
  38. 5. Minimizing Churn Rate Through Analysis of Financial Habits/1. Introduction.mp419.6MB
  39. 5. Minimizing Churn Rate Through Analysis of Financial Habits/2. Data.mp477.48MB
  40. 5. Minimizing Churn Rate Through Analysis of Financial Habits/3. Data Cleaning.mp432.56MB
  41. 5. Minimizing Churn Rate Through Analysis of Financial Habits/4. Features Histograms.mp450.24MB
  42. 5. Minimizing Churn Rate Through Analysis of Financial Habits/5. Pie Chart Distributions.mp470.58MB
  43. 5. Minimizing Churn Rate Through Analysis of Financial Habits/6. Correlation Plot.mp448.74MB
  44. 5. Minimizing Churn Rate Through Analysis of Financial Habits/7. Correlation Matrix.mp456.1MB
  45. 5. Minimizing Churn Rate Through Analysis of Financial Habits/8. One-Hot Encoding.mp442.89MB
  46. 5. Minimizing Churn Rate Through Analysis of Financial Habits/9. Feature Scaling & Balancing.mp479.72MB
  47. 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/10. Model Building Part 2.mp493.45MB
  48. 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/11. Grid Search Part 1.mp474.43MB
  49. 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/12. Grid Search Part 2.mp498.07MB
  50. 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/13. Model Conclusion.mp418.31MB
  51. 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/14. Final Remarks.mp430.57MB
  52. 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/1. Introduction.mp475.13MB
  53. 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/2. Data.mp4101.91MB
  54. 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/3. Data Housekeeping.mp444.08MB
  55. 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/4. Histograms.mp450.81MB
  56. 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/5. Correlation Plot.mp424.28MB
  57. 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/6. Correlation Matrix.mp433.41MB
  58. 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/7. Feature Engineering.mp423.47MB
  59. 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/8. Data Preprocessing.mp460.17MB
  60. 6. Predicting the Likelihood of E-Signing a Loan Based on Financial History/9. Model Building Part 1.mp458.77MB
  61. 7. Credit Card Fraud Detection/10. Metrics.mp415.11MB
  62. 7. Credit Card Fraud Detection/11. Confusion Matrix.mp440.06MB
  63. 7. Credit Card Fraud Detection/12. Machine Learning Classifiers.mp434.17MB
  64. 7. Credit Card Fraud Detection/13. Random Forest.mp431.02MB
  65. 7. Credit Card Fraud Detection/14. Decision Trees.mp418.77MB
  66. 7. Credit Card Fraud Detection/15. Sampling.mp47.74MB
  67. 7. Credit Card Fraud Detection/16. Undersampling.mp436.92MB
  68. 7. Credit Card Fraud Detection/17. Smote.mp435.71MB
  69. 7. Credit Card Fraud Detection/18. Final remarks.mp419.06MB
  70. 7. Credit Card Fraud Detection/1. Case Study.mp430.08MB
  71. 7. Credit Card Fraud Detection/2. Machine Learning Vocabulary.mp422.95MB
  72. 7. Credit Card Fraud Detection/3. Set Up.mp423.92MB
  73. 7. Credit Card Fraud Detection/4. Data Visualization.mp420.4MB
  74. 7. Credit Card Fraud Detection/5. Data Preprocessing.mp439.03MB
  75. 7. Credit Card Fraud Detection/6. Deep Learning Part 1.mp423.22MB
  76. 7. Credit Card Fraud Detection/7. Deep Learning Part 2.mp448.19MB
  77. 7. Credit Card Fraud Detection/8. Splitting the Data.mp438.21MB
  78. 7. Credit Card Fraud Detection/9. Training.mp421.19MB
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