种子简介
种子名称：
[FreeCoursesOnline.Me] Coursera - Applied Machine Learning in Python
文件类型：
视频
文件数目：
35个文件
文件大小：
880.52 MB
收录时间：
2018-12-9 03:36
已经下载：
3次
资源热度：
150
最近下载：
2024-12-14 20:54
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种子包含的文件
[FreeCoursesOnline.Me] Coursera - Applied Machine Learning in Python.torrent
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/001. Introduction.mp431.05MB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/002. Key Concepts in Machine Learning.mp444.56MB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/003. Python Tools for Machine Learning.mp412.86MB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/004. An Example Machine Learning Problem.mp431.73MB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/005. Examining the Data.mp432.24MB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/006. K-Nearest Neighbors Classification.mp436.25MB
002.Module 2 Supervised Machine Learning/007. Introduction to Supervised Machine Learning.mp437.88MB
002.Module 2 Supervised Machine Learning/008. Overfitting and Underfitting.mp419.51MB
002.Module 2 Supervised Machine Learning/009. Supervised Learning Datasets.mp411.22MB
002.Module 2 Supervised Machine Learning/010. K-Nearest Neighbors Classification and Regression.mp422.53MB
002.Module 2 Supervised Machine Learning/011. Linear Regression Least-Squares.mp430.08MB
002.Module 2 Supervised Machine Learning/012. Linear Regression Ridge, Lasso, and Polynomial Regression.mp439.93MB
002.Module 2 Supervised Machine Learning/013. Logistic Regression.mp420.3MB
002.Module 2 Supervised Machine Learning/014. Linear Classifiers Support Vector Machines.mp422.69MB
002.Module 2 Supervised Machine Learning/015. Multi-Class Classification.mp415.41MB
002.Module 2 Supervised Machine Learning/016. Kernelized Support Vector Machines.mp439.14MB
002.Module 2 Supervised Machine Learning/017. Cross-Validation.mp420MB
002.Module 2 Supervised Machine Learning/018. Decision Trees.mp437.83MB
003.Module 3 Evaluation/019. Model Evaluation & Selection.mp446.1MB
003.Module 3 Evaluation/020. Confusion Matrices & Basic Evaluation Metrics.mp420.75MB
003.Module 3 Evaluation/021. Classifier Decision Functions.mp412.65MB
003.Module 3 Evaluation/022. Precision-recall and ROC curves.mp49.23MB
003.Module 3 Evaluation/023. Multi-Class Evaluation.mp419.77MB
003.Module 3 Evaluation/024. Regression Evaluation.mp417.01MB
003.Module 3 Evaluation/025. Model Selection Optimizing Classifiers for Different Evaluation Metrics.mp434.5MB
004.Module 4 Supervised Machine Learning - Part 2/026. Naive Bayes Classifiers.mp421.38MB
004.Module 4 Supervised Machine Learning - Part 2/027. Random Forests.mp426.45MB
004.Module 4 Supervised Machine Learning - Part 2/028. Gradient Boosted Decision Trees.mp411.81MB
004.Module 4 Supervised Machine Learning - Part 2/029. Neural Networks.mp441.51MB
004.Module 4 Supervised Machine Learning - Part 2/030. Deep Learning (Optional).mp417.46MB
004.Module 4 Supervised Machine Learning - Part 2/031. Data Leakage.mp432.89MB
005.Optional Unsupervised Machine Learning/032. Introduction.mp410.67MB
005.Optional Unsupervised Machine Learning/033. Dimensionality Reduction and Manifold Learning.mp416.09MB
005.Optional Unsupervised Machine Learning/034. Clustering.mp427.18MB
006.Conclusion/035. Conclusion.mp49.89MB
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