Index of /torrents/[FreeCoursesOnline.Me] Coursera - Machine Learning/


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001.Welcome/                                       07-Sep-2018 02:18                   -
002.Introduction/                                  07-Sep-2018 02:18                   -
003.Model and Cost Function/                       07-Sep-2018 02:18                   -
004.Parameter Learning/                            07-Sep-2018 02:18                   -
005.Linear Algebra Review/                         07-Sep-2018 02:18                   -
006.Multivariate Linear Regression/                07-Sep-2018 02:18                   -
007.Computing Parameters Analytically/             07-Sep-2018 02:18                   -
008.Submitting Programming Assignments/            07-Sep-2018 02:18                   -
009.Octave Matlab Tutorial/                        07-Sep-2018 02:18                   -
010.Classification and Representation/             07-Sep-2018 02:18                   -
011.Logistic Regression Model/                     07-Sep-2018 02:18                   -
012.Multiclass Classification/                     07-Sep-2018 02:18                   -
013.Solving the Problem of Overfitting/            07-Sep-2018 02:18                   -
014.Motivations/                                   07-Sep-2018 02:18                   -
015.Neural Networks/                               07-Sep-2018 02:18                   -
016.Applications/                                  07-Sep-2018 02:18                   -
017.Cost Function and Backpropagation/             07-Sep-2018 02:18                   -
018.Backpropagation in Practice/                   07-Sep-2018 02:18                   -
019.Application of Neural Networks/                07-Sep-2018 02:18                   -
020.Evaluating a Learning Algorithm/               07-Sep-2018 02:18                   -
021.Bias vs. Variance/                             07-Sep-2018 02:18                   -
022.Building a Spam Classifier/                    07-Sep-2018 02:18                   -
023.Handling Skewed Data/                          07-Sep-2018 02:18                   -
024.Using Large Data Sets/                         07-Sep-2018 02:18                   -
025.Large Margin Classification/                   07-Sep-2018 02:18                   -
026.Kernels/                                       07-Sep-2018 02:18                   -
027.SVMs in Practice/                              07-Sep-2018 02:18                   -
028.Clustering/                                    07-Sep-2018 02:18                   -
029.Motivation/                                    07-Sep-2018 02:18                   -
030.Principal Component Analysis/                  07-Sep-2018 02:18                   -
031.Applying PCA/                                  07-Sep-2018 02:18                   -
032.Density Estimation/                            07-Sep-2018 02:18                   -
033.Building an Anomaly Detection System/          07-Sep-2018 02:18                   -
034.Multivariate Gaussian Distribution (Optional)/ 07-Sep-2018 02:18                   -
035.Predicting Movie Ratings/                      07-Sep-2018 02:18                   -
036.Collaborative Filtering/                       07-Sep-2018 02:18                   -
037.Low Rank Matrix Factorization/                 07-Sep-2018 02:18                   -
038.Gradient Descent with Large Datasets/          07-Sep-2018 02:18                   -
039.Advanced Topics/                               07-Sep-2018 02:18                   -
040.Photo OCR/                                     07-Sep-2018 02:18                   -
041.Conclusion/                                    07-Sep-2018 02:18                   -
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