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

磁力链接/BT种子名称

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

磁力链接/BT种子简介

种子哈希：2aebbd9a938b03ea4de16737994cb85b9fbdfd68
文件大小：881.06M
已经下载：3469次
下载速度：极快
收录时间：2020-01-24
最近下载：2026-06-15

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文件列表

  • 003.Module 3 Evaluation/019. Model Evaluation & Selection.mp4 48.3 MB
  • 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/002. Key Concepts in Machine Learning.mp4 46.7 MB
  • 004.Module 4 Supervised Machine Learning - Part 2/029. Neural Networks.mp4 43.5 MB
  • 002.Module 2 Supervised Machine Learning/012. Linear Regression Ridge, Lasso, and Polynomial Regression.mp4 41.9 MB
  • 002.Module 2 Supervised Machine Learning/016. Kernelized Support Vector Machines.mp4 41.0 MB
  • 002.Module 2 Supervised Machine Learning/007. Introduction to Supervised Machine Learning.mp4 39.7 MB
  • 002.Module 2 Supervised Machine Learning/018. Decision Trees.mp4 39.7 MB
  • 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/006. K-Nearest Neighbors Classification.mp4 38.0 MB
  • 003.Module 3 Evaluation/025. Model Selection Optimizing Classifiers for Different Evaluation Metrics.mp4 36.2 MB
  • 004.Module 4 Supervised Machine Learning - Part 2/031. Data Leakage.mp4 34.5 MB
  • 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/005. Examining the Data.mp4 33.8 MB
  • 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/004. An Example Machine Learning Problem.mp4 33.3 MB
  • 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/001. Introduction.mp4 32.6 MB
  • 002.Module 2 Supervised Machine Learning/011. Linear Regression Least-Squares.mp4 31.5 MB
  • 005.Optional Unsupervised Machine Learning/034. Clustering.mp4 28.5 MB
  • 004.Module 4 Supervised Machine Learning - Part 2/027. Random Forests.mp4 27.7 MB
  • 002.Module 2 Supervised Machine Learning/014. Linear Classifiers Support Vector Machines.mp4 23.8 MB
  • 002.Module 2 Supervised Machine Learning/010. K-Nearest Neighbors Classification and Regression.mp4 23.6 MB
  • 004.Module 4 Supervised Machine Learning - Part 2/026. Naive Bayes Classifiers.mp4 22.4 MB
  • 003.Module 3 Evaluation/020. Confusion Matrices & Basic Evaluation Metrics.mp4 21.8 MB
  • 002.Module 2 Supervised Machine Learning/013. Logistic Regression.mp4 21.3 MB
  • 002.Module 2 Supervised Machine Learning/017. Cross-Validation.mp4 21.0 MB
  • 003.Module 3 Evaluation/023. Multi-Class Evaluation.mp4 20.7 MB
  • 002.Module 2 Supervised Machine Learning/008. Overfitting and Underfitting.mp4 20.5 MB
  • 004.Module 4 Supervised Machine Learning - Part 2/030. Deep Learning (Optional).mp4 18.3 MB
  • 003.Module 3 Evaluation/024. Regression Evaluation.mp4 17.8 MB
  • 005.Optional Unsupervised Machine Learning/033. Dimensionality Reduction and Manifold Learning.mp4 16.9 MB
  • 002.Module 2 Supervised Machine Learning/015. Multi-Class Classification.mp4 16.2 MB
  • 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/003. Python Tools for Machine Learning.mp4 13.5 MB
  • 003.Module 3 Evaluation/021. Classifier Decision Functions.mp4 13.3 MB
  • 004.Module 4 Supervised Machine Learning - Part 2/028. Gradient Boosted Decision Trees.mp4 12.4 MB
  • 002.Module 2 Supervised Machine Learning/009. Supervised Learning Datasets.mp4 11.8 MB
  • 005.Optional Unsupervised Machine Learning/032. Introduction.mp4 11.2 MB
  • 006.Conclusion/035. Conclusion.mp4 10.4 MB
  • 003.Module 3 Evaluation/022. Precision-recall and ROC curves.mp4 9.7 MB
  • 003.Module 3 Evaluation/019. Model Evaluation & Selection.srt 30.8 kB
  • 002.Module 2 Supervised Machine Learning/018. Decision Trees.srt 29.0 kB
  • 004.Module 4 Supervised Machine Learning - Part 2/029. Neural Networks.srt 28.6 kB
  • 002.Module 2 Supervised Machine Learning/012. Linear Regression Ridge, Lasso, and Polynomial Regression.srt 27.8 kB
  • 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/006. K-Nearest Neighbors Classification.srt 26.8 kB
  • 002.Module 2 Supervised Machine Learning/016. Kernelized Support Vector Machines.srt 26.2 kB
  • 002.Module 2 Supervised Machine Learning/007. Introduction to Supervised Machine Learning.srt 22.7 kB
  • 002.Module 2 Supervised Machine Learning/011. Linear Regression Least-Squares.srt 21.8 kB
  • 005.Optional Unsupervised Machine Learning/034. Clustering.srt 20.4 kB
  • 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/002. Key Concepts in Machine Learning.srt 19.3 kB
  • 003.Module 3 Evaluation/025. Model Selection Optimizing Classifiers for Different Evaluation Metrics.srt 18.6 kB
  • 002.Module 2 Supervised Machine Learning/013. Logistic Regression.srt 17.5 kB
  • 002.Module 2 Supervised Machine Learning/010. K-Nearest Neighbors Classification and Regression.srt 17.5 kB
  • 004.Module 4 Supervised Machine Learning - Part 2/027. Random Forests.srt 17.5 kB
  • 004.Module 4 Supervised Machine Learning - Part 2/031. Data Leakage.srt 17.1 kB
  • 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/001. Introduction.srt 16.5 kB
  • 003.Module 3 Evaluation/020. Confusion Matrices & Basic Evaluation Metrics.srt 16.2 kB
  • 002.Module 2 Supervised Machine Learning/008. Overfitting and Underfitting.srt 16.2 kB
  • 002.Module 2 Supervised Machine Learning/014. Linear Classifiers Support Vector Machines.srt 15.9 kB
  • 003.Module 3 Evaluation/023. Multi-Class Evaluation.srt 15.6 kB
  • 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/004. An Example Machine Learning Problem.srt 15.2 kB
  • 005.Optional Unsupervised Machine Learning/033. Dimensionality Reduction and Manifold Learning.srt 13.8 kB
  • 002.Module 2 Supervised Machine Learning/017. Cross-Validation.srt 13.3 kB
  • 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/005. Examining the Data.srt 12.3 kB
  • 004.Module 4 Supervised Machine Learning - Part 2/026. Naive Bayes Classifiers.srt 11.5 kB
  • 004.Module 4 Supervised Machine Learning - Part 2/030. Deep Learning (Optional).srt 10.6 kB
  • 003.Module 3 Evaluation/021. Classifier Decision Functions.srt 9.3 kB
  • 004.Module 4 Supervised Machine Learning - Part 2/028. Gradient Boosted Decision Trees.srt 8.6 kB
  • 002.Module 2 Supervised Machine Learning/015. Multi-Class Classification.srt 8.5 kB
  • 003.Module 3 Evaluation/024. Regression Evaluation.srt 8.0 kB
  • 003.Module 3 Evaluation/022. Precision-recall and ROC curves.srt 7.7 kB
  • 002.Module 2 Supervised Machine Learning/009. Supervised Learning Datasets.srt 6.9 kB
  • 005.Optional Unsupervised Machine Learning/032. Introduction.srt 6.6 kB
  • 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/003. Python Tools for Machine Learning.srt 6.3 kB
  • 006.Conclusion/035. Conclusion.srt 4.0 kB
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