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

Creation Time	Aug. 25, 2023, 7:58 p.m.
Last Access Time	Dec. 21, 2024, 8 a.m.
File Size	381.0 MB
Keywords	FreeCoursesOnline Me Applied LYNDA Machine Learning Foundations FCO
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3.2. Exploratory Data Analysis and Data Cleaning/12.Exploring continuous features.mp4 24.2 MB
6.5. End-to-End Pipeline/35.Final model selection and evaluation on test set.mp4 24.1 MB
1.Introduction/01.Leveraging machine learning.mp4 19.1 MB
6.5. End-to-End Pipeline/34.Evaluate results on validation set.mp4 18.5 MB
6.5. End-to-End Pipeline/33.Tune hyperparameters.mp4 18.1 MB
3.2. Exploratory Data Analysis and Data Cleaning/13.Plotting continuous features.mp4 17.9 MB
3.2. Exploratory Data Analysis and Data Cleaning/15.Exploring categorical features.mp4 15.1 MB
3.2. Exploratory Data Analysis and Data Cleaning/14.Continuous data cleaning.mp4 15.1 MB
6.5. End-to-End Pipeline/32.Fit a basic model using cross-validation.mp4 14.9 MB
3.2. Exploratory Data Analysis and Data Cleaning/16.Plotting categorical features.mp4 14.3 MB
6.5. End-to-End Pipeline/29.Clean continuous features.mp4 13.8 MB
4.3. Measuring Success/19.Split data for train_validation_test set.mp4 13.0 MB
2.1. Machine Learning Basics/07.Why Python.mp4 12.1 MB
3.2. Exploratory Data Analysis and Data Cleaning/17.Categorical data cleaning.mp4 11.0 MB
6.5. End-to-End Pipeline/30.Clean categorical features.mp4 10.6 MB
2.1. Machine Learning Basics/09.Demos of machine learning in real life.mp4 10.6 MB
6.5. End-to-End Pipeline/31.Split data into train_validation_test set.mp4 9.7 MB
5.4. Optimizing a Model/26.Hyperparameter tuning.mp4 9.6 MB
4.3. Measuring Success/18.Why do we split up our data.mp4 9.5 MB
4.3. Measuring Success/20.What is cross-validation.mp4 9.0 MB
2.1. Machine Learning Basics/10.Common challenges.mp4 9.0 MB
2.1. Machine Learning Basics/06.What kind of problems can this help you solve.mp4 8.3 MB
5.4. Optimizing a Model/22.Bias_Variance tradeoff.mp4 8.1 MB
4.3. Measuring Success/21.Establish an evaluation framework.mp4 7.0 MB
2.1. Machine Learning Basics/08.Machine learning vs. Deep learning vs. Artificial intelligence.mp4 6.9 MB
7.Conclusion/36.Next steps.mp4 6.2 MB
2.1. Machine Learning Basics/05.What is machine learning.mp4 6.0 MB
5.4. Optimizing a Model/25.Finding the optimal tradeoff.mp4 5.4 MB
3.2. Exploratory Data Analysis and Data Cleaning/11.Why do we need to explore and clean our data.mp4 5.2 MB
5.4. Optimizing a Model/24.What is overfitting.mp4 4.6 MB
1.Introduction/02.What you should know.mp4 4.5 MB
5.4. Optimizing a Model/27.Regularization.mp4 4.4 MB
5.4. Optimizing a Model/23.What is underfitting.mp4 4.0 MB
Exercise Files/Ex_Files_Applied_Machine_Learning.zip 3.4 MB
1.Introduction/04.Using the exercise files.mp4 3.1 MB
6.5. End-to-End Pipeline/28.Overview of the process.mp4 2.6 MB
1.Introduction/03.What tools you need.mp4 1.6 MB
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Discuss.FTUForum.com.html 31.9 KB
How you can help Team-FTU.txt 235 bytes
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[FreeCoursesOnline.Me] [LYNDA] Applied Machine Learning Foundations [FCO]

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