[CourseClub.NET] Coursera - Applied Machine Learning in Python
| 文件类型 | 收录时间 | 最后活跃 | 资源热度 | 文件大小 | 文件数量 |
|---|---|---|---|---|---|
| 视频 | 2020-6-25 08:14 | 2024-12-19 08:01 | 169 | 880.52 MB | 35 |
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文件列表
- 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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