[CourseClub.NET] Coursera - Applied Machine Learning in Python

Creation Time	April 20, 2024, 7:06 p.m.
Last Access Time	Dec. 21, 2024, 1:04 a.m.
File Size	881.1 MB
Keywords	Applied CourseClub Python Coursera - Machine Learning in NET
Total Requests	62
Total Files	73
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003.Module 3 Evaluation/019. Model Evaluation & Selection.mp4 46.1 MB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/002. Key Concepts in Machine Learning.mp4 44.6 MB
004.Module 4 Supervised Machine Learning - Part 2/029. Neural Networks.mp4 41.5 MB
002.Module 2 Supervised Machine Learning/012. Linear Regression Ridge, Lasso, and Polynomial Regression.mp4 39.9 MB
002.Module 2 Supervised Machine Learning/016. Kernelized Support Vector Machines.mp4 39.1 MB
002.Module 2 Supervised Machine Learning/007. Introduction to Supervised Machine Learning.mp4 37.9 MB
002.Module 2 Supervised Machine Learning/018. Decision Trees.mp4 37.8 MB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/006. K-Nearest Neighbors Classification.mp4 36.2 MB
003.Module 3 Evaluation/025. Model Selection Optimizing Classifiers for Different Evaluation Metrics.mp4 34.5 MB
004.Module 4 Supervised Machine Learning - Part 2/031. Data Leakage.mp4 32.9 MB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/005. Examining the Data.mp4 32.2 MB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/004. An Example Machine Learning Problem.mp4 31.7 MB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/001. Introduction.mp4 31.1 MB
002.Module 2 Supervised Machine Learning/011. Linear Regression Least-Squares.mp4 30.1 MB
005.Optional Unsupervised Machine Learning/034. Clustering.mp4 27.2 MB
004.Module 4 Supervised Machine Learning - Part 2/027. Random Forests.mp4 26.4 MB
002.Module 2 Supervised Machine Learning/014. Linear Classifiers Support Vector Machines.mp4 22.7 MB
002.Module 2 Supervised Machine Learning/010. K-Nearest Neighbors Classification and Regression.mp4 22.5 MB
004.Module 4 Supervised Machine Learning - Part 2/026. Naive Bayes Classifiers.mp4 21.4 MB
003.Module 3 Evaluation/020. Confusion Matrices & Basic Evaluation Metrics.mp4 20.8 MB
002.Module 2 Supervised Machine Learning/013. Logistic Regression.mp4 20.3 MB
002.Module 2 Supervised Machine Learning/017. Cross-Validation.mp4 20.0 MB
003.Module 3 Evaluation/023. Multi-Class Evaluation.mp4 19.8 MB
002.Module 2 Supervised Machine Learning/008. Overfitting and Underfitting.mp4 19.5 MB
004.Module 4 Supervised Machine Learning - Part 2/030. Deep Learning (Optional).mp4 17.5 MB
003.Module 3 Evaluation/024. Regression Evaluation.mp4 17.0 MB
005.Optional Unsupervised Machine Learning/033. Dimensionality Reduction and Manifold Learning.mp4 16.1 MB
002.Module 2 Supervised Machine Learning/015. Multi-Class Classification.mp4 15.4 MB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/003. Python Tools for Machine Learning.mp4 12.9 MB
003.Module 3 Evaluation/021. Classifier Decision Functions.mp4 12.7 MB
004.Module 4 Supervised Machine Learning - Part 2/028. Gradient Boosted Decision Trees.mp4 11.8 MB
002.Module 2 Supervised Machine Learning/009. Supervised Learning Datasets.mp4 11.2 MB
005.Optional Unsupervised Machine Learning/032. Introduction.mp4 10.7 MB
006.Conclusion/035. Conclusion.mp4 9.9 MB
003.Module 3 Evaluation/022. Precision-recall and ROC curves.mp4 9.2 MB
003.Module 3 Evaluation/019. Model Evaluation & Selection.srt 30.1 KB
002.Module 2 Supervised Machine Learning/018. Decision Trees.srt 28.4 KB
004.Module 4 Supervised Machine Learning - Part 2/029. Neural Networks.srt 27.9 KB
002.Module 2 Supervised Machine Learning/012. Linear Regression Ridge, Lasso, and Polynomial Regression.srt 27.2 KB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/006. K-Nearest Neighbors Classification.srt 26.2 KB
002.Module 2 Supervised Machine Learning/016. Kernelized Support Vector Machines.srt 25.6 KB
002.Module 2 Supervised Machine Learning/007. Introduction to Supervised Machine Learning.srt 22.1 KB
002.Module 2 Supervised Machine Learning/011. Linear Regression Least-Squares.srt 21.3 KB
005.Optional Unsupervised Machine Learning/034. Clustering.srt 19.9 KB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/002. Key Concepts in Machine Learning.srt 18.8 KB
003.Module 3 Evaluation/025. Model Selection Optimizing Classifiers for Different Evaluation Metrics.srt 18.1 KB
002.Module 2 Supervised Machine Learning/013. Logistic Regression.srt 17.1 KB
002.Module 2 Supervised Machine Learning/010. K-Nearest Neighbors Classification and Regression.srt 17.1 KB
004.Module 4 Supervised Machine Learning - Part 2/027. Random Forests.srt 17.1 KB
004.Module 4 Supervised Machine Learning - Part 2/031. Data Leakage.srt 16.7 KB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/001. Introduction.srt 16.1 KB
003.Module 3 Evaluation/020. Confusion Matrices & Basic Evaluation Metrics.srt 15.8 KB
002.Module 2 Supervised Machine Learning/008. Overfitting and Underfitting.srt 15.8 KB
002.Module 2 Supervised Machine Learning/014. Linear Classifiers Support Vector Machines.srt 15.5 KB
003.Module 3 Evaluation/023. Multi-Class Evaluation.srt 15.2 KB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/004. An Example Machine Learning Problem.srt 14.8 KB
005.Optional Unsupervised Machine Learning/033. Dimensionality Reduction and Manifold Learning.srt 13.5 KB
002.Module 2 Supervised Machine Learning/017. Cross-Validation.srt 13.0 KB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/005. Examining the Data.srt 12.1 KB
004.Module 4 Supervised Machine Learning - Part 2/026. Naive Bayes Classifiers.srt 11.2 KB
004.Module 4 Supervised Machine Learning - Part 2/030. Deep Learning (Optional).srt 10.3 KB
003.Module 3 Evaluation/021. Classifier Decision Functions.srt 9.0 KB
004.Module 4 Supervised Machine Learning - Part 2/028. Gradient Boosted Decision Trees.srt 8.4 KB
002.Module 2 Supervised Machine Learning/015. Multi-Class Classification.srt 8.3 KB
003.Module 3 Evaluation/024. Regression Evaluation.srt 7.8 KB
003.Module 3 Evaluation/022. Precision-recall and ROC curves.srt 7.5 KB
002.Module 2 Supervised Machine Learning/009. Supervised Learning Datasets.srt 6.7 KB
005.Optional Unsupervised Machine Learning/032. Introduction.srt 6.5 KB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/003. Python Tools for Machine Learning.srt 6.1 KB
006.Conclusion/035. Conclusion.srt 3.9 KB
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[CourseClub.NET] Coursera - Applied Machine Learning in Python

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