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[FreeCoursesOnline.Me] [LYNDA] Applied Machine Learning Foundations [FCO]

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视频 2021-5-8 20:46 2024-11-29 17:14 291 377.31 MB 36
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文件列表
  1. 1.Introduction/01.Leveraging machine learning.mp419.14MB
  2. 1.Introduction/02.What you should know.mp44.49MB
  3. 1.Introduction/03.What tools you need.mp41.62MB
  4. 1.Introduction/04.Using the exercise files.mp43.06MB
  5. 2.1. Machine Learning Basics/05.What is machine learning.mp45.98MB
  6. 2.1. Machine Learning Basics/06.What kind of problems can this help you solve.mp48.31MB
  7. 2.1. Machine Learning Basics/07.Why Python.mp412.14MB
  8. 2.1. Machine Learning Basics/08.Machine learning vs. Deep learning vs. Artificial intelligence.mp46.87MB
  9. 2.1. Machine Learning Basics/09.Demos of machine learning in real life.mp410.55MB
  10. 2.1. Machine Learning Basics/10.Common challenges.mp48.98MB
  11. 3.2. Exploratory Data Analysis and Data Cleaning/11.Why do we need to explore and clean our data.mp45.2MB
  12. 3.2. Exploratory Data Analysis and Data Cleaning/12.Exploring continuous features.mp424.23MB
  13. 3.2. Exploratory Data Analysis and Data Cleaning/13.Plotting continuous features.mp417.86MB
  14. 3.2. Exploratory Data Analysis and Data Cleaning/14.Continuous data cleaning.mp415.07MB
  15. 3.2. Exploratory Data Analysis and Data Cleaning/15.Exploring categorical features.mp415.14MB
  16. 3.2. Exploratory Data Analysis and Data Cleaning/16.Plotting categorical features.mp414.29MB
  17. 3.2. Exploratory Data Analysis and Data Cleaning/17.Categorical data cleaning.mp411.02MB
  18. 4.3. Measuring Success/18.Why do we split up our data.mp49.49MB
  19. 4.3. Measuring Success/19.Split data for train_validation_test set.mp412.99MB
  20. 4.3. Measuring Success/20.What is cross-validation.mp49.04MB
  21. 4.3. Measuring Success/21.Establish an evaluation framework.mp46.98MB
  22. 5.4. Optimizing a Model/22.Bias_Variance tradeoff.mp48.11MB
  23. 5.4. Optimizing a Model/23.What is underfitting.mp44.04MB
  24. 5.4. Optimizing a Model/24.What is overfitting.mp44.61MB
  25. 5.4. Optimizing a Model/25.Finding the optimal tradeoff.mp45.45MB
  26. 5.4. Optimizing a Model/26.Hyperparameter tuning.mp49.63MB
  27. 5.4. Optimizing a Model/27.Regularization.mp44.41MB
  28. 6.5. End-to-End Pipeline/28.Overview of the process.mp42.57MB
  29. 6.5. End-to-End Pipeline/29.Clean continuous features.mp413.79MB
  30. 6.5. End-to-End Pipeline/30.Clean categorical features.mp410.62MB
  31. 6.5. End-to-End Pipeline/31.Split data into train_validation_test set.mp49.71MB
  32. 6.5. End-to-End Pipeline/32.Fit a basic model using cross-validation.mp414.91MB
  33. 6.5. End-to-End Pipeline/33.Tune hyperparameters.mp418.15MB
  34. 6.5. End-to-End Pipeline/34.Evaluate results on validation set.mp418.55MB
  35. 6.5. End-to-End Pipeline/35.Final model selection and evaluation on test set.mp424.12MB
  36. 7.Conclusion/36.Next steps.mp46.19MB
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