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[FreeCoursesOnline.Me] [LYNDA] Applied Machine Learning Foundations [FCO]
magnet:?xt=urn:btih:fa6fa3238e15b9281ac82c9a493db3a9a43faffe&dn=[FreeCoursesOnline.Me] [LYNDA] Applied Machine Learning Foundations [FCO]
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文件列表详情
fa6fa3238e15b9281ac82c9a493db3a9a43faffe
infohash:
36
文件数量
377.31 MB
文件大小
2021-5-8 20:46
创建日期
2024-9-30 05:13
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相关分词
FreeCoursesOnline
Me
LYNDA
Applied
Machine
Learning
Foundations
FCO
1.Introduction/01.Leveraging machine learning.mp4 19.14 MB
1.Introduction/02.What you should know.mp4 4.49 MB
1.Introduction/03.What tools you need.mp4 1.62 MB
1.Introduction/04.Using the exercise files.mp4 3.06 MB
2.1. Machine Learning Basics/05.What is machine learning.mp4 5.98 MB
2.1. Machine Learning Basics/06.What kind of problems can this help you solve.mp4 8.31 MB
2.1. Machine Learning Basics/07.Why Python.mp4 12.14 MB
2.1. Machine Learning Basics/08.Machine learning vs. Deep learning vs. Artificial intelligence.mp4 6.87 MB
2.1. Machine Learning Basics/09.Demos of machine learning in real life.mp4 10.55 MB
2.1. Machine Learning Basics/10.Common challenges.mp4 8.98 MB
3.2. Exploratory Data Analysis and Data Cleaning/11.Why do we need to explore and clean our data.mp4 5.2 MB
3.2. Exploratory Data Analysis and Data Cleaning/12.Exploring continuous features.mp4 24.23 MB
3.2. Exploratory Data Analysis and Data Cleaning/13.Plotting continuous features.mp4 17.86 MB
3.2. Exploratory Data Analysis and Data Cleaning/14.Continuous data cleaning.mp4 15.07 MB
3.2. Exploratory Data Analysis and Data Cleaning/15.Exploring categorical features.mp4 15.14 MB
3.2. Exploratory Data Analysis and Data Cleaning/16.Plotting categorical features.mp4 14.29 MB
3.2. Exploratory Data Analysis and Data Cleaning/17.Categorical data cleaning.mp4 11.02 MB
4.3. Measuring Success/18.Why do we split up our data.mp4 9.49 MB
4.3. Measuring Success/19.Split data for train_validation_test set.mp4 12.99 MB
4.3. Measuring Success/20.What is cross-validation.mp4 9.04 MB
4.3. Measuring Success/21.Establish an evaluation framework.mp4 6.98 MB
5.4. Optimizing a Model/22.Bias_Variance tradeoff.mp4 8.11 MB
5.4. Optimizing a Model/23.What is underfitting.mp4 4.04 MB
5.4. Optimizing a Model/24.What is overfitting.mp4 4.61 MB
5.4. Optimizing a Model/25.Finding the optimal tradeoff.mp4 5.45 MB
5.4. Optimizing a Model/26.Hyperparameter tuning.mp4 9.63 MB
5.4. Optimizing a Model/27.Regularization.mp4 4.41 MB
6.5. End-to-End Pipeline/28.Overview of the process.mp4 2.57 MB
6.5. End-to-End Pipeline/29.Clean continuous features.mp4 13.79 MB
6.5. End-to-End Pipeline/30.Clean categorical features.mp4 10.62 MB
6.5. End-to-End Pipeline/31.Split data into train_validation_test set.mp4 9.71 MB
6.5. End-to-End Pipeline/32.Fit a basic model using cross-validation.mp4 14.91 MB
6.5. End-to-End Pipeline/33.Tune hyperparameters.mp4 18.15 MB
6.5. End-to-End Pipeline/34.Evaluate results on validation set.mp4 18.55 MB
6.5. End-to-End Pipeline/35.Final model selection and evaluation on test set.mp4 24.12 MB
7.Conclusion/36.Next steps.mp4 6.19 MB
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