Coursera - Applied Machine Learning in Python

磁力链接/BT种子名称

Coursera - Applied Machine Learning in Python

磁力链接/BT种子简介

种子哈希：dd767a23d92100eaf9f60ce697a88119ae396976
文件大小：540.47M
已经下载：701次
下载速度：极快
收录时间：2024-04-16
最近下载：2025-10-13

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文件列表

  • 03_module-3/01_module-3-evaluation/01_model-evaluation-selection.mp4 33.3 MB
  • 02_module-2/01_module-2-supervised-machine-learning/05_linear-regression-least-squares.mp4 31.8 MB
  • 02_module-2/01_module-2-supervised-machine-learning/06_linear-regression-ridge-lasso-and-polynomial-regression.mp4 30.7 MB
  • 02_module-2/01_module-2-supervised-machine-learning/12_decision-trees.mp4 28.8 MB
  • 04_module-4/01_module-4-supervised-machine-learning-part-2/05_neural-networks.mp4 28.4 MB
  • 01_module-1/01_module-1-fundamentals-of-machine-learning-intro-to-scikit-learn/07_k-nearest-neighbors-classification.mp4 28.2 MB
  • 01_module-1/01_module-1-fundamentals-of-machine-learning-intro-to-scikit-learn/03_key-concepts-in-machine-learning.mp4 24.9 MB
  • 03_module-3/01_module-3-evaluation/08_model-selection-optimizing-classifiers-for-different-evaluation-metrics.mp4 21.0 MB
  • 04_module-4/01_module-4-supervised-machine-learning-part-2/08_data-leakage.mp4 20.1 MB
  • 01_module-1/01_module-1-fundamentals-of-machine-learning-intro-to-scikit-learn/05_an-example-machine-learning-problem.mp4 20.0 MB
  • 02_module-2/01_module-2-supervised-machine-learning/01_introduction-to-supervised-machine-learning.mp4 19.9 MB
  • 02_module-2/01_module-2-supervised-machine-learning/08_linear-classifiers-support-vector-machines.mp4 19.2 MB
  • 02_module-2/01_module-2-supervised-machine-learning/04_k-nearest-neighbors-classification-and-regression.mp4 18.7 MB
  • 01_module-1/01_module-1-fundamentals-of-machine-learning-intro-to-scikit-learn/02_introduction.mp4 18.3 MB
  • 04_module-4/01_module-4-supervised-machine-learning-part-2/03_random-forests.mp4 18.3 MB
  • 03_module-3/01_module-3-evaluation/05_multi-class-evaluation.mp4 17.5 MB
  • 02_module-2/01_module-2-supervised-machine-learning/07_logistic-regression.mp4 17.2 MB
  • 03_module-3/01_module-3-evaluation/02_confusion-matrices-basic-evaluation-metrics.mp4 17.0 MB
  • 01_module-1/01_module-1-fundamentals-of-machine-learning-intro-to-scikit-learn/06_examining-the-data.mp4 16.4 MB
  • 02_module-2/01_module-2-supervised-machine-learning/02_overfitting-and-underfitting.mp4 16.3 MB
  • 02_module-2/01_module-2-supervised-machine-learning/11_cross-validation.mp4 13.5 MB
  • 04_module-4/01_module-4-supervised-machine-learning-part-2/02_naive-bayes-classifiers.mp4 12.9 MB
  • 02_module-2/01_module-2-supervised-machine-learning/10_kernalized-support-vector-machines.mp4 12.7 MB
  • 04_module-4/01_module-4-supervised-machine-learning-part-2/06_deep-learning-optional.mp4 11.3 MB
  • 02_module-2/01_module-2-supervised-machine-learning/09_multi-class-classification.mp4 10.4 MB
  • 03_module-3/01_module-3-evaluation/03_classifier-decision-functions.mp4 10.4 MB
  • 03_module-3/01_module-3-evaluation/06_regression-evaluation.mp4 10.1 MB
  • 04_module-4/01_module-4-supervised-machine-learning-part-2/04_gradient-boosted-decision-trees.mp4 8.9 MB
  • 03_module-3/01_module-3-evaluation/04_precision-recall-and-roc-curves.mp4 8.5 MB
  • 01_module-1/01_module-1-fundamentals-of-machine-learning-intro-to-scikit-learn/04_python-tools-for-machine-learning.mp4 8.1 MB
  • 02_module-2/01_module-2-supervised-machine-learning/03_supervised-learning-datasets.mp4 7.6 MB
  • 04_module-4/01_module-4-supervised-machine-learning-part-2/01_introduction.mp4 4.8 MB
  • 03_module-3/01_module-3-evaluation/07_practical-guide-to-controlled-experiments-on-the-web.pdf 504.9 kB
  • 02_module-2/01_module-2-supervised-machine-learning/13_a-few-useful-things-to-know-about-machine-learning_cacm12.pdf 160.2 kB
  • 03_module-3/01_module-3-evaluation/01_model-evaluation-selection.en.srt 30.8 kB
  • 02_module-2/01_module-2-supervised-machine-learning/12_decision-trees.en.srt 29.0 kB
  • 04_module-4/01_module-4-supervised-machine-learning-part-2/05_neural-networks.en.srt 28.6 kB
  • 02_module-2/01_module-2-supervised-machine-learning/05_linear-regression-least-squares.en.srt 28.2 kB
  • 02_module-2/01_module-2-supervised-machine-learning/06_linear-regression-ridge-lasso-and-polynomial-regression.en.srt 27.8 kB
  • 01_module-1/01_module-1-fundamentals-of-machine-learning-intro-to-scikit-learn/07_k-nearest-neighbors-classification.en.srt 26.8 kB
  • 01_module-1/01_module-1-fundamentals-of-machine-learning-intro-to-scikit-learn/03_key-concepts-in-machine-learning.en.srt 19.3 kB
  • 03_module-3/01_module-3-evaluation/08_model-selection-optimizing-classifiers-for-different-evaluation-metrics.en.srt 18.3 kB
  • 02_module-2/01_module-2-supervised-machine-learning/07_logistic-regression.en.srt 17.5 kB
  • 02_module-2/01_module-2-supervised-machine-learning/04_k-nearest-neighbors-classification-and-regression.en.srt 17.5 kB
  • 02_module-2/01_module-2-supervised-machine-learning/01_introduction-to-supervised-machine-learning.en.srt 17.5 kB
  • 04_module-4/01_module-4-supervised-machine-learning-part-2/03_random-forests.en.srt 17.5 kB
  • 04_module-4/01_module-4-supervised-machine-learning-part-2/08_data-leakage.en.srt 17.1 kB
  • 01_module-1/01_module-1-fundamentals-of-machine-learning-intro-to-scikit-learn/02_introduction.en.srt 16.5 kB
  • 03_module-3/01_module-3-evaluation/02_confusion-matrices-basic-evaluation-metrics.en.srt 16.2 kB
  • 02_module-2/01_module-2-supervised-machine-learning/02_overfitting-and-underfitting.en.srt 16.2 kB
  • 02_module-2/01_module-2-supervised-machine-learning/08_linear-classifiers-support-vector-machines.en.srt 15.9 kB
  • 03_module-3/01_module-3-evaluation/05_multi-class-evaluation.en.srt 15.6 kB
  • 01_module-1/01_module-1-fundamentals-of-machine-learning-intro-to-scikit-learn/05_an-example-machine-learning-problem.en.srt 15.2 kB
  • 02_module-2/01_module-2-supervised-machine-learning/11_cross-validation.en.srt 13.3 kB
  • 01_module-1/01_module-1-fundamentals-of-machine-learning-intro-to-scikit-learn/06_examining-the-data.en.srt 12.3 kB
  • 04_module-4/01_module-4-supervised-machine-learning-part-2/02_naive-bayes-classifiers.en.srt 11.5 kB
  • 04_module-4/01_module-4-supervised-machine-learning-part-2/06_deep-learning-optional.en.srt 10.6 kB
  • 02_module-2/01_module-2-supervised-machine-learning/10_kernalized-support-vector-machines.en.srt 10.1 kB
  • 03_module-3/01_module-3-evaluation/03_classifier-decision-functions.en.srt 9.3 kB
  • 04_module-4/01_module-4-supervised-machine-learning-part-2/04_gradient-boosted-decision-trees.en.srt 8.6 kB
  • 02_module-2/01_module-2-supervised-machine-learning/09_multi-class-classification.en.srt 8.5 kB
  • 03_module-3/01_module-3-evaluation/06_regression-evaluation.en.srt 8.0 kB
  • 03_module-3/01_module-3-evaluation/04_precision-recall-and-roc-curves.en.srt 7.7 kB
  • 02_module-2/01_module-2-supervised-machine-learning/03_supervised-learning-datasets.en.srt 6.9 kB
  • 01_module-1/01_module-1-fundamentals-of-machine-learning-intro-to-scikit-learn/04_python-tools-for-machine-learning.en.srt 6.3 kB
  • 04_module-4/01_module-4-supervised-machine-learning-part-2/01_introduction.en.srt 4.7 kB
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