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[FreeCoursesOnline.Me] [LYNDA] Applied Machine Learning Foundations [FCO]
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2021-5-8 20:46
2024-11-29 17:14
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377.31 MB
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磁力链接
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迅雷链接
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FreeCoursesOnline
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Applied
Machine
Learning
Foundations
FCO
文件列表
1.Introduction/01.Leveraging machine learning.mp4
19.14MB
1.Introduction/02.What you should know.mp4
4.49MB
1.Introduction/03.What tools you need.mp4
1.62MB
1.Introduction/04.Using the exercise files.mp4
3.06MB
2.1. Machine Learning Basics/05.What is machine learning.mp4
5.98MB
2.1. Machine Learning Basics/06.What kind of problems can this help you solve.mp4
8.31MB
2.1. Machine Learning Basics/07.Why Python.mp4
12.14MB
2.1. Machine Learning Basics/08.Machine learning vs. Deep learning vs. Artificial intelligence.mp4
6.87MB
2.1. Machine Learning Basics/09.Demos of machine learning in real life.mp4
10.55MB
2.1. Machine Learning Basics/10.Common challenges.mp4
8.98MB
3.2. Exploratory Data Analysis and Data Cleaning/11.Why do we need to explore and clean our data.mp4
5.2MB
3.2. Exploratory Data Analysis and Data Cleaning/12.Exploring continuous features.mp4
24.23MB
3.2. Exploratory Data Analysis and Data Cleaning/13.Plotting continuous features.mp4
17.86MB
3.2. Exploratory Data Analysis and Data Cleaning/14.Continuous data cleaning.mp4
15.07MB
3.2. Exploratory Data Analysis and Data Cleaning/15.Exploring categorical features.mp4
15.14MB
3.2. Exploratory Data Analysis and Data Cleaning/16.Plotting categorical features.mp4
14.29MB
3.2. Exploratory Data Analysis and Data Cleaning/17.Categorical data cleaning.mp4
11.02MB
4.3. Measuring Success/18.Why do we split up our data.mp4
9.49MB
4.3. Measuring Success/19.Split data for train_validation_test set.mp4
12.99MB
4.3. Measuring Success/20.What is cross-validation.mp4
9.04MB
4.3. Measuring Success/21.Establish an evaluation framework.mp4
6.98MB
5.4. Optimizing a Model/22.Bias_Variance tradeoff.mp4
8.11MB
5.4. Optimizing a Model/23.What is underfitting.mp4
4.04MB
5.4. Optimizing a Model/24.What is overfitting.mp4
4.61MB
5.4. Optimizing a Model/25.Finding the optimal tradeoff.mp4
5.45MB
5.4. Optimizing a Model/26.Hyperparameter tuning.mp4
9.63MB
5.4. Optimizing a Model/27.Regularization.mp4
4.41MB
6.5. End-to-End Pipeline/28.Overview of the process.mp4
2.57MB
6.5. End-to-End Pipeline/29.Clean continuous features.mp4
13.79MB
6.5. End-to-End Pipeline/30.Clean categorical features.mp4
10.62MB
6.5. End-to-End Pipeline/31.Split data into train_validation_test set.mp4
9.71MB
6.5. End-to-End Pipeline/32.Fit a basic model using cross-validation.mp4
14.91MB
6.5. End-to-End Pipeline/33.Tune hyperparameters.mp4
18.15MB
6.5. End-to-End Pipeline/34.Evaluate results on validation set.mp4
18.55MB
6.5. End-to-End Pipeline/35.Final model selection and evaluation on test set.mp4
24.12MB
7.Conclusion/36.Next steps.mp4
6.19MB
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