[FreeCoursesOnline.Me] [LYNDA] Applied Machine Learning Foundations [FCO]
| 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 | 2021-5-8 20:46 | 2024-5-18 00:01 | 220 | 377.31 MB | 36 |
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文件列表
- 1.Introduction/01.Leveraging machine learning.mp419.14MB
- 1.Introduction/02.What you should know.mp44.49MB
- 1.Introduction/03.What tools you need.mp41.62MB
- 1.Introduction/04.Using the exercise files.mp43.06MB
- 2.1. Machine Learning Basics/05.What is machine learning.mp45.98MB
- 2.1. Machine Learning Basics/06.What kind of problems can this help you solve.mp48.31MB
- 2.1. Machine Learning Basics/07.Why Python.mp412.14MB
- 2.1. Machine Learning Basics/08.Machine learning vs. Deep learning vs. Artificial intelligence.mp46.87MB
- 2.1. Machine Learning Basics/09.Demos of machine learning in real life.mp410.55MB
- 2.1. Machine Learning Basics/10.Common challenges.mp48.98MB
- 3.2. Exploratory Data Analysis and Data Cleaning/11.Why do we need to explore and clean our data.mp45.2MB
- 3.2. Exploratory Data Analysis and Data Cleaning/12.Exploring continuous features.mp424.23MB
- 3.2. Exploratory Data Analysis and Data Cleaning/13.Plotting continuous features.mp417.86MB
- 3.2. Exploratory Data Analysis and Data Cleaning/14.Continuous data cleaning.mp415.07MB
- 3.2. Exploratory Data Analysis and Data Cleaning/15.Exploring categorical features.mp415.14MB
- 3.2. Exploratory Data Analysis and Data Cleaning/16.Plotting categorical features.mp414.29MB
- 3.2. Exploratory Data Analysis and Data Cleaning/17.Categorical data cleaning.mp411.02MB
- 4.3. Measuring Success/18.Why do we split up our data.mp49.49MB
- 4.3. Measuring Success/19.Split data for train_validation_test set.mp412.99MB
- 4.3. Measuring Success/20.What is cross-validation.mp49.04MB
- 4.3. Measuring Success/21.Establish an evaluation framework.mp46.98MB
- 5.4. Optimizing a Model/22.Bias_Variance tradeoff.mp48.11MB
- 5.4. Optimizing a Model/23.What is underfitting.mp44.04MB
- 5.4. Optimizing a Model/24.What is overfitting.mp44.61MB
- 5.4. Optimizing a Model/25.Finding the optimal tradeoff.mp45.45MB
- 5.4. Optimizing a Model/26.Hyperparameter tuning.mp49.63MB
- 5.4. Optimizing a Model/27.Regularization.mp44.41MB
- 6.5. End-to-End Pipeline/28.Overview of the process.mp42.57MB
- 6.5. End-to-End Pipeline/29.Clean continuous features.mp413.79MB
- 6.5. End-to-End Pipeline/30.Clean categorical features.mp410.62MB
- 6.5. End-to-End Pipeline/31.Split data into train_validation_test set.mp49.71MB
- 6.5. End-to-End Pipeline/32.Fit a basic model using cross-validation.mp414.91MB
- 6.5. End-to-End Pipeline/33.Tune hyperparameters.mp418.15MB
- 6.5. End-to-End Pipeline/34.Evaluate results on validation set.mp418.55MB
- 6.5. End-to-End Pipeline/35.Final model selection and evaluation on test set.mp424.12MB
- 7.Conclusion/36.Next steps.mp46.19MB
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