Coursera-Applied-chine-L-1LGLA
001.Module 1 Fundamentals of chine Learning - Intro to SciKit Learn\\/001. Introduction.mp431.05MB
001.Module 1 Fundamentals of chine Learning - Intro to SciKit Learn\\/002. Key Concepts in Machine Learning.mp444.56MB
001.Module 1 Fundamentals of chine Learning - Intro to SciKit Learn\\/003. Python Tools for Machine Learning.mp412.86MB
001.Module 1 Fundamentals of chine Learning - Intro to SciKit Learn\\/004. An Example Machine Learning Problem.mp431.73MB
001.Module 1 Fundamentals of chine Learning - Intro to SciKit Learn\\/005. Examining the Data.mp432.24MB
001.Module 1 Fundamentals of chine Learning - Intro to SciKit Learn\\/006. K-Nearest Neighbors Classification.mp436.25MB
002.Module 2 Supervised chine Learning\\/007. Introduction to Supervised Machine Learning.mp437.88MB
002.Module 2 Supervised chine Learning\\/008. Overfitting and Underfitting.mp419.51MB
002.Module 2 Supervised chine Learning\\/009. Supervised Learning Datasets.mp411.22MB
002.Module 2 Supervised chine Learning\\/010. K-Nearest Neighbors Classification and Regression.mp422.53MB
002.Module 2 Supervised chine Learning\\/011. Linear Regression Least-Squares.mp430.08MB
002.Module 2 Supervised chine Learning\\/012. Linear Regression Ridge Lasso and Polynomial Regression.mp439.93MB
002.Module 2 Supervised chine Learning\\/013. Logistic Regression.mp420.30MB
002.Module 2 Supervised chine Learning\\/014. Linear Classifiers Support Vector Machines.mp422.69MB
002.Module 2 Supervised chine Learning\\/015. Multi-Class Classification.mp415.41MB
002.Module 2 Supervised chine Learning\\/016. Kernelized Support Vector Machines.mp439.14MB
002.Module 2 Supervised chine Learning\\/017. Cross-Validation.mp420.00MB
002.Module 2 Supervised chine Learning\\/018. Decision Trees.mp437.83MB
003.Module 3 Evaluation\\/019. Model Evaluation & Selection.mp446.10MB
003.Module 3 Evaluation\\/020. Confusion trices & 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.mp434.50MB
004.Module 4 Supervised chine Learning - Part 2\\/026. Naive Bayes Classifiers.mp421.38MB
004.Module 4 Supervised chine Learning - Part 2\\/027. Random Forests.mp426.45MB
004.Module 4 Supervised chine Learning - Part 2\\/028. Gradient Boosted Decision Trees.mp411.81MB
004.Module 4 Supervised chine Learning - Part 2\\/029. Neural Networks.mp441.51MB
004.Module 4 Supervised chine Learning - Part 2\\/030. Deep Learning (Optional).mp417.46MB
004.Module 4 Supervised chine Learning - Part 2\\/031. Data Leakage.mp432.89MB
005.Optional Unsupervised chine Learning\\/032. Introduction.mp410.67MB
005.Optional Unsupervised chine Learning\\/033. Dimensionality Reduction and Manifold Learning.mp416.09MB
005.Optional Unsupervised chine Learning\\/034. Clustering.mp427.18MB
006.Conclusion\\/035. Conclusion.mp49.89MB
- CreateTime2022-10-05
- UpdateTime2022-10-13
- FileTotalCount73
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